Average dice coefficients of the few-supervised learning models using 2%, 5%, and 10% of the labeled data, and semi-supervised learning models using 10% of the labeled data for training.

AI in Unsupervised Learning Market Outlook

According to our latest research, the AI in Unsupervised Learning market size reached USD 3.8 billion globally in 2024, demonstrating robust expansion as organizations increasingly leverage unsupervised techniques for extracting actionable insights from unlabelled data. The market is forecasted to grow at a CAGR of 28.2% from 2025 to 2033, propelling the industry to an estimated USD 36.7 billion by 2033. This remarkable growth trajectory is primarily fueled by the escalating adoption of artificial intelligence across diverse sectors, an exponential surge in data generation, and the pressing need for advanced analytics that can operate without manual data labeling.

One of the key growth factors driving the AI in Unsupervised Learning market is the rising complexity and volume of data generated by enterprises in the digital era. Organizations are inundated with unstructured and unlabelled data from sources such as social media, IoT devices, and transactional systems. Traditional supervised learning methods are often impractical due to the time and cost associated with manual labeling. Unsupervised learning algorithms, such as clustering and dimensionality reduction, offer a scalable solution by autonomously identifying patterns, anomalies, and hidden structures within vast datasets. This capability is increasingly vital for industries aiming to enhance decision-making, streamline operations, and gain a competitive edge through advanced analytics.

Another significant driver is the rapid advancement in computational power and AI infrastructure, which has made it feasible to implement sophisticated unsupervised learning models at scale. The proliferation of cloud computing and specialized AI hardware has reduced barriers to entry, enabling even small and medium enterprises to deploy unsupervised learning solutions. Additionally, the evolution of neural networks and deep learning architectures has expanded the scope of unsupervised algorithms, allowing for more complex tasks such as image recognition, natural language processing, and anomaly detection. These technological advancements are not only accelerating adoption but also fostering innovation across sectors including healthcare, finance, manufacturing, and retail.

Furthermore, regulatory compliance and the growing emphasis on data privacy are pushing organizations to adopt unsupervised learning methods. Unlike supervised approaches that require sensitive data labeling, unsupervised algorithms can process data without explicit human intervention, thereby reducing the risk of privacy breaches. This is particularly relevant in sectors such as healthcare and BFSI, where stringent data protection regulations are in place. The ability to derive insights from unlabelled data while maintaining compliance is a compelling value proposition, further propelling the market forward.

Regionally, North America continues to dominate the AI in Unsupervised Learning market owing to its advanced technological ecosystem, significant investments in AI research, and strong presence of leading market players. Europe follows closely, driven by robust regulatory frameworks and a focus on ethical AI deployment. The Asia Pacific region is exhibiting the fastest growth, fueled by rapid digital transformation, government initiatives, and increasing adoption of AI across industries. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as awareness and infrastructure continue to develop.

Component Analysis

The Component segment of the AI in Unsupervised Learning market is categorized into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. The software segment, comprising machine learning frameworks, data analytics platforms, and AI development tools, holds the largest market share. This dominance is attributed to the continuous evolution of AI algorithms and the increasing availability of open-source and proprietary solutions tailored for unsupervised learning. Enterprises are investing heavily in software that can facilitate the seamless integration of unsupervised learning capabilities into existing workflows, enabling automation, predictive analytics, and pattern recognition without the need for labeled data.

The hardware segment, while smaller in comparison to software, is experiencing significant growth due to the escalating demand for high-perf

Machine learning techniques that rely on textual features or sentiment lexicons can lead to erroneous sentiment analysis. These techniques are especially vulnerable to domain-related difficulties, especially when dealing in Big data. In addition, labeling is time-consuming and supervised machine learning algorithms often lack labeled data. Transfer learning can help save time and obtain high performance with fewer datasets in this field. To cope this, we used a transfer learning-based Multi-Domain Sentiment Classification (MDSC) technique. We are able to identify the sentiment polarity of text in a target domain that is unlabeled by looking at reviews in a labelled source domain. This research aims to evaluate the impact of domain adaptation and measure the extent to which transfer learning enhances sentiment analysis outcomes. We employed transfer learning models BERT, RoBERTa, ELECTRA, and ULMFiT to improve the performance in sentiment analysis. We analyzed sentiment through various transformer models and compared the performance of LSTM and CNN. The experiments are carried on five publicly available sentiment analysis datasets, namely Hotel Reviews (HR), Movie Reviews (MR), Sentiment140 Tweets (ST), Citation Sentiment Corpus (CSC), and Bioinformatics Citation Corpus (BCC), to adapt multi-target domains. The performance of numerous models employing transfer learning from diverse datasets demonstrating how various factors influence the outputs.

Analysis of ‘BLE RSSI Dataset for Indoor localization’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mehdimka/ble-rssi-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

The dataset was created using the RSSI readings of an array of 13 ibeacons in the first floor of Waldo Library, Western Michigan University. Data was collected using iPhone 6S. The dataset contains two sub-datasets: a labeled dataset (1420 instances) and an unlabeled dataset (5191 instances). The recording was performed during the operational hours of the library. For the labeled dataset, the input data contains the location (label column), a timestamp, followed by RSSI readings of 13 iBeacons. RSSI measurements are negative values. Bigger RSSI values indicate closer proximity to a given iBeacon (e.g., RSSI of -65 represent a closer distance to a given iBeacon compared to RSSI of -85). For out-of-range iBeacons, the RSSI is indicated by -200. The locations related to RSSI readings are combined in one column consisting a letter for the column and a number for the row of the position. The following figure depicts the layout of the iBeacons as well as the arrange of locations.

https://www.kaggle.com/mehdimka/ble-rssi-dataset/downloads/iBeacon_Layout.jpg" alt="iBeacons Layout">

Attribute Information

• location: The location of receiving RSSIs from ibeacons b3001 to b3013; symbolic values showing the column and row of the location on the map (e.g., A01 stands for column A, row 1).
• date: Datetime in the format of ‘d-m-yyyy hh:mm:ss’
• b3001 - b3013: RSSI readings corresponding to the iBeacons; numeric, integers only.

Acknowledgements

Provider: Mehdi Mohammadi and Ala Al-Fuqaha, {mehdi.mohammadi, ala-alfuqaha}@wmich.edu, Department of Computer Science, Western Michigan University

Citation Request:

M. Mohammadi, A. Al-Fuqaha, M. Guizani, J. Oh, “Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services,” IEEE Internet of Things Journal, Vol. PP, No. 99, 2017.

Inspiration

How unlabeled data can help for an improved learning system. How a GAN model can synthesizes viable paths based on the little labeled data and larger set of unlabeled data.

--- Original source retains full ownership of the source dataset ---

The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances. Labelling is performed with PC-Annotate and can easily be extended by the end-users employing the same tool.The data is organized into unlabelled and labelled 3D point clouds. The unlabelled data is provided in .PCAP file format, which is the direct output format of the used Ouster LiDAR sensor. Raw frames are extracted from the recorded .PCAP files in the form of Ply and Excel files using the Ouster Studio Software. Labelled 3D point cloud data consists of registered or raw point clouds. A labelled point cloud is a combination of Ply, Excel, Labels and Summary files. A point cloud in Ply file contains X, Y, Z values along with color information. An Excel file contains X, Y, Z values, Intensity, Reflectivity, Ring, Noise, and Range of each point. These attributes can be useful in semantic segmentation using deep learning algorithms. The Label and Label Summary files have been explained in the previous section. Our one GB raw data contains nearly 1,300 raw frames, whereas 66,425 frames are provided in the dataset, each comprising 65,536 points. Hence, 4.3 billion points captured with the Ouster LiDAR sensor are provided. Annotation of 25 general outdoor classes is provided, which include car, building, bridge, tree, road, letterbox, traffic signal, light-pole, rubbish bin, cycles, motorcycle, truck, bus, bushes, road sign board, advertising board, road divider, road lane, pedestrians, side-path, wall, bus stop, water, zebra-crossing, and background. With the released data, a total of 143 scenes are annotated which include both raw and registered frames.

AI in Semi-supervised Learning Market Outlook

According to our latest research, the AI in Semi-supervised Learning market size reached USD 1.82 billion in 2024 globally, driven by rapid advancements in artificial intelligence and machine learning applications across diverse industries. The market is expected to expand at a robust CAGR of 28.1% from 2025 to 2033, reaching a projected value of USD 17.17 billion by 2033. This exponential growth is primarily fueled by the increasing need for efficient data labeling, the proliferation of unstructured data, and the growing adoption of AI-driven solutions in both large enterprises and small and medium businesses. As per the latest research, the surging demand for automation, accuracy, and cost-efficiency in data processing is significantly accelerating the adoption of semi-supervised learning models worldwide.

One of the most significant growth factors for the AI in Semi-supervised Learning market is the explosive increase in data generation across industries such as healthcare, finance, retail, and automotive. Organizations are continually collecting vast amounts of structured and unstructured data, but the process of labeling this data for supervised learning remains time-consuming and expensive. Semi-supervised learning offers a compelling solution by leveraging small amounts of labeled data alongside large volumes of unlabeled data, thus reducing the dependency on extensive manual annotation. This approach not only accelerates the deployment of AI models but also enhances their accuracy and scalability, making it highly attractive for enterprises seeking to maximize the value of their data assets while minimizing operational costs.

Another critical driver propelling the growth of the AI in Semi-supervised Learning market is the increasing sophistication of AI algorithms and the integration of advanced technologies such as deep learning, natural language processing, and computer vision. These advancements have enabled semi-supervised learning models to achieve remarkable performance in complex tasks like image and speech recognition, medical diagnostics, and fraud detection. The ability to process and interpret vast datasets with minimal supervision is particularly valuable in sectors where labeled data is scarce or expensive to obtain. Furthermore, the ongoing investments in research and development by leading technology companies and academic institutions are fostering innovation, resulting in more robust and scalable semi-supervised learning frameworks that can be seamlessly integrated into enterprise workflows.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments are also contributing significantly to the expansion of the AI in Semi-supervised Learning market. Cloud-based deployment offers unparalleled scalability, flexibility, and cost-efficiency, allowing organizations of all sizes to access cutting-edge AI tools and infrastructure without the need for substantial upfront investments. This democratization of AI technology is empowering small and medium enterprises to leverage semi-supervised learning for competitive advantage, driving widespread adoption across regions and industries. Additionally, the emergence of AI-as-a-Service (AIaaS) platforms is further simplifying the integration and management of semi-supervised learning models, enabling businesses to accelerate their digital transformation initiatives and unlock new growth opportunities.

From a regional perspective, North America currently dominates the AI in Semi-supervised Learning market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI vendors, robust technological infrastructure, and high investments in AI research and development are key factors driving market growth in these regions. Asia Pacific is expected to witness the fastest CAGR during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing government initiatives to promote AI adoption. Meanwhile, Latin America and the Middle East & Africa are also showing promising growth potential, supported by rising awareness of AI benefits and growing investments in digital transformation projects across various sectors.

Component Analysis

The component segment of the AI in Semi-supervised Learning market is divided into software, hardware, and services, each playing a pivotal role in the adoption and implementation of semi-s

DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data.

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in: labeled data (500CT+100MRI) unlabeled data Part I (900CT) unlabeled data Part II (1100CT) (Now there are 1000CT, we will replenish to 1100CT) unlabeled data Part III (1200MRI) if you found this dataset useful for your research, please cite: @inproceedings{NEURIPS2022_ee604e1b, author = {Ji, Yuanfeng and Bai, Haotian and GE, Chongjian and Yang, Jie and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhanng, Lingyan and Ma, Wanling and Wan, Xiang and Luo, Ping}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {36722--36732}, publisher = {Curran Associates, Inc.}, title = {AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/ee604e1bedbd069d9fc9328b7b9584be-Paper-Datasets_and_Benchmarks.pdf}, volume = {35}, year = {2022} }

Number of images used for the training and testing of the models with different labeling strategies.

LEPset is a large-scale EUS-based pancreas image dataset from the Department of Goenterology, Changhai Hospital, Second Military Medical University/Naval Medical University. This dataset consists of 420 patients and 3,500 images, and it has been divided into two categories (PC and NPC). We have invited experienced clinicians to annotate the category labels for all 3500 EUS images. Moreover, our LEPset also has 8,000 EUS images without any classification annotation.

1. After downloading the data set LEPset.zip, select the appropriate unzip file to extract it
2. After unzipping, there will be two folders: unlabeled and labeled
3. There are 8000 EUS images in the unlabeled folder and two folders in the labeled folder, NPC and PC, representing non-pancreatic cancer and pancreatic cancer respectively. 140 patients (1820 images) in NPC and 280 patients (1680 images) in PC
4. Unlabelled images can be used for pre-training of the model, and labelled images can be used for training and validation of the supervised model

This item is part of the collection "AIS Trajectories from Danish Waters for Abnormal Behavior Detection"

DOI: https://doi.org/10.11583/DTU.c.6287841

Using Deep Learning for detection of maritime abnormal behaviour in spatio temporal trajectories is a relatively new and promising application. Open access to the Automatic Identification System (AIS) has made large amounts of maritime trajectories publically avaliable. However, these trajectories are unannotated when it comes to the detection of abnormal behaviour.

The lack of annotated datasets for abnormality detection on maritime trajectories makes it difficult to evaluate and compare suggested models quantitavely. With this dataset, we attempt to provide a way for researchers to evaluate and compare performance.

We have manually labelled trajectories which showcase abnormal behaviour following an collision accident. The annotated dataset consists of 521 data points with 25 abnormal trajectories. The abnormal trajectories cover amoung other; Colliding vessels, vessels engaged in Search-and-Rescue activities, law enforcement, and commercial maritime traffic forced to deviate from the normal course

These datasets consists of unlabelled trajectories for the purpose of training unsupervised models. For labelled datasets for evaluation please refer to the collection. Link in Related publications.

The data is saved using the pickle format for Python Each dataset is split into 2 files with naming convention:

datasetInfo_XXX
data_XXX

Files named "data_XXX" contains the extracted trajectories serialized sequentially one at a time and must be read as such. Please refer to provided utility functions for examples. Files named "datasetInfo" contains Metadata related to the dataset and indecies at which trajectories begin in "data_XXX" files.

The data are sequences of maritime trajectories defined by their; timestamp, latitude/longitude position, speed, course, and unique ship identifer MMSI. In addition, the dataset contains metadata related to creation parameters. The dataset has been limited to a specific time period, ship types, moving AIS navigational statuses, and filtered within an region of interest (ROI). Trajectories were split if exceeding an upper limit and short trajectories were discarded. All values are given as metadata in the dataset and used in the naming syntax.

Naming syntax: data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl

See datasheet for more detailed information and we refer to provided utility functions for examples on how to read and plot the data.

![]() The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods. Overview 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. Images are 96x96 pixels, color. 500 training images (10 pre-defined folds), 800 test images per class. 100000 unlabeled images for uns

Once literature triage system is ready it is time to actually try to apply if to records that do not have any label in order to find the subset that does describe TF-TG interactions (are relevant). This is the corpus that has to be labeled by the systems created (hopefully) during the hackathon. To make the results more useful we have pre-selected records that do mention TFs by exploiting either automatic human TF mention recognition or external references from databases that have manually curated information on transcription factors (from GeneRif or UniProt). This means that these abstracts should be enriched with TF relevant records. This record has the same format as the training data except that the last column with the class label is missing.It contains PMIDs and Abstracts.Name: greekc_triage_unlabelled_v01.tsvExample:Format: tsv-separated columns (PMID, PubAnnotation JSON formated results of Pubtator for this record together with the automatically detected gene mentions using GnormPlus providing the Entrez Gene Identifiers together with the mention offsets, i.e. start and end character positionsPubAnnotation format description: http://www.pubannotation.org/docs/annotation-format/PubTator record retrieval description:https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/curl.htmlWarning: This file is quite big!

Machine Learning Courses Market Outlook

The global market size of Machine Learning (ML) courses is witnessing substantial growth, with market valuation expected to reach $3.1 billion in 2023 and projected to soar to $12.6 billion by 2032, exhibiting a robust CAGR of 16.5% over the forecast period. This rapid expansion is fueled by the increasing adoption of artificial intelligence (AI) and machine learning technologies across various industries, the rising need for upskilling and reskilling in the workforce, and the growing penetration of online education platforms.

One of the most significant growth factors driving the ML courses market is the escalating demand for AI and ML expertise in the job market. As industries increasingly integrate AI and machine learning into their operations to enhance efficiency and innovation, there is a burgeoning need for professionals with relevant skills. Companies across sectors such as finance, healthcare, retail, and manufacturing are investing heavily in training programs to bridge the skills gap, thus driving the demand for ML courses. Additionally, the rapid evolution of technology necessitates continuous learning, further bolstering market growth.

Another crucial factor contributing to the market's expansion is the proliferation of online education platforms that offer flexible and affordable ML courses. Platforms like Coursera, Udacity, edX, and Khan Academy have made high-quality education accessible to a global audience. These platforms offer an array of courses tailored to different skill levels, from beginners to advanced learners, making it easier for individuals to pursue continuous learning and career advancement. The convenience and flexibility of online learning are particularly appealing to working professionals and students, thereby driving the market's growth.

The increasing collaboration between educational institutions and technology companies is also playing a pivotal role in the growth of the ML courses market. Many universities and colleges are partnering with leading tech firms to develop specialized curricula that align with industry requirements. These collaborations help ensure that the courses offered are up-to-date with the latest technological advancements and industry standards. As a result, students and professionals are better equipped with the skills needed to thrive in a technology-driven job market, further propelling the demand for ML courses.

On a regional level, North America holds a significant share of the ML courses market, driven by the presence of numerous leading tech companies and educational institutions, as well as a highly skilled workforce. The region's strong emphasis on innovation and technological advancement is a key driver of market growth. Additionally, Asia Pacific is emerging as a lucrative market for ML courses, with countries like China, India, and Japan witnessing increased investments in AI and ML education and training. The rising internet penetration, growing popularity of online education, and government initiatives to promote digital literacy are some of the factors contributing to the market's growth in this region.

Self-Supervised Learning, a cutting-edge approach in the realm of machine learning, is gaining traction as a pivotal element in the development of more autonomous AI systems. Unlike traditional supervised learning, which relies heavily on labeled data, self-supervised learning leverages unlabeled data to train models, significantly reducing the dependency on human intervention for data annotation. This method is particularly advantageous in scenarios where acquiring labeled data is costly or impractical. By enabling models to learn from vast amounts of unlabeled data, self-supervised learning enhances the ability of AI systems to generalize from limited labeled examples, thereby improving their performance in real-world applications. The integration of self-supervised learning techniques into machine learning courses is becoming increasingly important, as it equips learners with the knowledge to tackle complex AI challenges and develop more robust models.

Course Type Analysis

The Machine Learning Courses market is segmented by course type into online courses, offline courses, bootcamps, and workshops. Online courses dominate the segment due to their accessibility, flexibility, and cost-effectiveness. Platforms like Coursera and Udacity have democratized access to high-quality ML education, enabling lear

This data set is a test data set containing one labelled and one unlabeled image. The full labelled and unlabeled data sets can be found on this site as: "Labelled Weed Detection Images for Hot Peppers" "Unlabeled Weed Detection Images for Hot Peppers"

The Concrete Aggregate Dataset consists of high resolution images acquired from 40 different concrete cylinders, cut lengthwise as to display the particle distribution in the concrete, with a ground sampling distance of 0.03mm. In order to train and evaluate approaches for the semantic segmentation of the concrete aggregate images, currently 17 of the 40 images have been annotated by manually associating one of the classes aggregate or suspension to each pixel. We encourage to use the remaining unlabelled images for semi-supervised segmentation approaches, in which unlabelled data is leveraged in addition to labelled training data in order to improve the segmentation performance.

In the subsequent figure, five examplary tiles of size 448x448 pixels and their annotated label masks are shown. The diversity of the appearance of both, aggregate and suspension can be noted. https://data.uni-hannover.de/dataset/afd56c85-b885-4731-af17-258838c6d728/resource/0a830d13-3e5e-450c-8bd3-b2052b015f58/download/dataset.png" alt="Reference CAD Models" title=" ">

In the figure below, the distribution of the aggregate particles in dependency on their sizes is depicted. The variation of the size of the particles contained in the data set ranges up to 15mm of maximum particle diameter. However, the majority of particles, namely more than 50% exhibit a maximum diameter of less then 3mm (100px). As a consequence, approximately 80% of the particles possess an area of 5mm ^2 or less.It has to be noted that particles with a size less then 20px are barely distinguishable from the suspension and are therefore not contained in the reference data. https://data.uni-hannover.de/dataset/afd56c85-b885-4731-af17-258838c6d728/resource/b5a18dc0-7236-440a-b3b5-504bd5fb6d70/download/particlestats.png" alt="Reference CAD Models" title=" ">

If you make use of the proposed data, please cite the publication listed below.

Related Publications:

• Coenen, M.; Schack, T.; Beyer, D.; Heipke, C. and Haist, M. (2021): Semi-Supervised Segmentation of Concrete Aggregate using Consensus Regularisation and Prior Guidance. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021, pp. 83–91, https://doi.org/10.5194/isprs-annals-V-2-2021-83-2021.

DC_inside_comments

This dataset contains 110,000 raw comments collected from DC Inside. It is intended for unsupervised learning or pretraining purposes.

  Dataset Summary

Data Type: Unlabeled raw comments Number of Examples: 110,000 Source: DC Inside

  Related Dataset

For labeled data and multi-task annotated examples, please refer to the KoMultiText dataset.

  How to Load the Dataset

from datasets import load_dataset

Load the unlabeled dataset… See the full description on the dataset page: https://huggingface.co/datasets/Dasool/DC_inside_comments.

This dataset contains microscopic images of multiple cell lines captured by multiple microcopic without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation.

Our data consist of

244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), in the paper designated as "QPI_Seg_PNT1A_PC3", and

1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, A8780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "QPI_Cell_unlabelled".

See Vicar et al. XXXX 2021 DOI XXX (TBA after publishing)

Code using this dataset is available at XXXX (TBA after publishing)

Materials and methods

A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, A8780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5% CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic microscope (Telight, Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart.

Folder structure and file and filename description

labelled (QPI_Seg_PNT1A_PC3): 205 FOVs PNT1A and 244 FOVs PC-3 cells with segmentation labels, e.g. 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values) 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV.

unlabelled (QPI_Cell_unlabelled): 11 varying cell lines, total 1819 FOVs, 32bit tiff image (in pg/um2 values)

Self-Supervised Learning Market Outlook

As of 2023, the global self-supervised learning market size is valued at approximately USD 1.5 billion and is expected to escalate to around USD 10.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 24.1% during the forecast period. This robust growth is driven by the increasing demand for advanced AI models that can learn from large volumes of unlabeled data, significantly reducing the dependency on labeled datasets, thereby making AI training more cost-effective and scalable.

The growth of the self-supervised learning market is fueled by several factors, one of which is the exponential increase in data generation. With the proliferation of digital devices, IoT technologies, and social media platforms, there is an unprecedented amount of data being created every second. Self-supervised learning models leverage this vast amount of unlabeled data to train themselves, making them particularly valuable in industries where data labeling is time-consuming and expensive. This capability is especially pertinent in fields like healthcare, finance, and retail, where the rapid analysis of extensive datasets can lead to significant advancements in predictive analytics and customer insights.

Another critical driver is the advancement in computational technologies that support more sophisticated machine learning models. The development of more powerful GPUs and cloud-based AI platforms has enabled the efficient training and deployment of self-supervised learning models. These technological advancements not only reduce the time required for training but also enhance the accuracy and performance of the models. Furthermore, the integration of self-supervised learning with other AI paradigms such as reinforcement learning and deep learning is opening new avenues for research and application, further propelling market growth.

The increasing adoption of AI across various industries is also a significant growth factor. Businesses are increasingly recognizing the potential of AI to optimize operations, enhance customer experiences, and drive innovation. Self-supervised learning, with its ability to make sense of large, unstructured datasets, is becoming a cornerstone of AI strategies across sectors. For instance, in the healthcare sector, self-supervised learning is being used to develop predictive models for disease diagnosis and treatment planning, while in the finance sector, it aids in fraud detection and risk management.

Regionally, North America is expected to dominate the self-supervised learning market, owing to the presence of leading technology companies and extensive R&D activities in AI. However, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by rapid digital transformation, increasing investment in AI technologies, and supportive government initiatives. Europe also presents a significant market opportunity, with a strong focus on AI research and development, particularly in countries like Germany, the UK, and France.

Component Analysis

The self-supervised learning market is segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share, driven by the development and adoption of advanced AI algorithms and platforms. These software solutions are designed to leverage the vast amounts of unlabeled data available, making them highly valuable for various applications such as natural language processing, computer vision, and predictive analytics. Furthermore, continuous advancements in software capabilities, such as improved model training techniques and enhanced data preprocessing tools, are expected to fuel the growth of this segment.

The hardware segment, while smaller in comparison to software, is crucial for the efficient deployment of self-supervised learning models. This includes high-performance computing systems, GPUs, and specialized AI accelerators that provide the necessary computational power to train and run complex AI models. Innovations in hardware technology, such as the development of more energy-efficient and powerful processing units, are expected to drive growth in this segment. Additionally, the increasing adoption of edge computing devices that can perform AI tasks locally, thereby reducing latency and bandwidth usage, is also contributing to the expansion of the hardware segment.

Services are another vital component of the self-supervised learning market. This segment encompasses various professional services such as consulting, int

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by graph convolutional network (GCN). The innovation of our model is leveraging data augmentation strategy to solve the dilemma of limited labeled data and using consistency regularization to optimize the prediction consistency of unlabeled data across different data augmentations. The results showed that HiRAND achieved better performance than competitive methods in various prediction scenarios, including both simulation data and multiple drug response data. We found that the prediction ability of HiRAND in the drug vorinostat showed the best results across all 62 drugs. In addition, HiRAND was interpreted to identify the key genes most important to vorinostat response, highlighting critical roles for ribosomal protein-related genes in the response to histone deacetylase inhibition. Our HiRAND could be utilized as an efficient framework for improving the drug response prediction performance using few labeled data.