Machine Vision (Validation)

## Overview

Machine Vision (Validation) is a dataset for classification tasks - it contains Tumor EOlu annotations for 255 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Validation of the supervised machine-learning based tool.

This is the database for full model training and evaluation for LPM

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

Background

New York City quickly became an epicenter of the COVID-19 pandemic. Due to a sudden and massive increase in patients during COVID-19 pandemic, healthcare providers incurred an exponential increase in workload which created a strain on the staff and limited resources. As this is a new infection, predictors of morbidity and mortality are not well characterized.

Methods

We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3000 hospital admissions with COVID-19. A variable importance algorithm was used for interpretability and understanding of performance and predictors.

Findings

We built a model with 84-97% accuracy to identify predictors and patients with high risk of mortality, and developed an automated artificial intelligence (AI) notification tool that does not require manual calculation by the busy clinician. Oximetry, respirations, blood urea nitrogen, lymphocyte percent, calcium, troponin and neutrophil percentage were important features and key ranges were identified that contributed to a 50% increase in patients’ mortality prediction score. With an increasing negative predictive value (NPV) starting 0.90 after the second day of admission, we are able more confidently able identify likely survivors. This study serves as a use case of a model with visualizations to aide clinicians with a better understanding of the model and predictors of mortality. Additionally, an example of the operationalization of the model via an AI notification tool is illustrated.

organized within the "ions" and "molecules" folders

Market Overview: The global medical device validation service market is projected to reach a value of USD 35.5 billion by 2033, exhibiting a CAGR of 11.7% during the forecast period 2025-2033. The market is driven by the increasing demand for advanced medical devices and the need for stringent regulatory compliance. Factors such as technological advancements, growing awareness of patient safety, and the rising prevalence of chronic diseases are further fueling market growth. Key Trends and Market Segmentation: Emerging trends in the market include the adoption of artificial intelligence (AI) and machine learning (ML) to improve the efficiency and accuracy of validation processes. The market is segmented by type into mechanical testing, biological testing, and electromagnetic compatibility (EMC) testing. Major application areas include medical device manufacturing, pharmaceutical, biotechnology, and research and development. Geographically, North America is the largest market, followed by Europe and Asia Pacific. The Medical Device Validation Service market is expected to reach USD 12.5 billion by 2027, growing at a CAGR of 6.5% from 2020 to 2027. The market is driven by the increasing demand for medical devices, stringent regulatory requirements, and the need for ensuring the safety and efficacy of medical devices.

Supplementary Material 4.

This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation, dissimilarity quantification method, and dissimilarity-adaptive cross-validation.

Cross-validation results of Support Vector Machine to predict pollen type, based on 99 iterations of random 80:20 splits of data into training:test sets.

Discover the booming Data Annotation & Model Validation Platform market! Learn about its $2B valuation, 25% CAGR, key drivers, and top players like Labelbox & CloudFactory. Explore regional insights & forecast to 2033. Invest wisely in this rapidly expanding AI sector.

Python scripts

The dataset consists of simulated and observed salinity/temperature data which were used in the manuscript "A method for assessment of the general circulation model quality using k-means clustering algorithm" submitted to Geoscientific Model Development.
The model simulation dataset is from long-term 3D circulation model simulation (Maljutenko and Raudsepp 2014, 2019). The observations are from the "Baltic Sea - Eutrophication and Acidity aggregated datasets 1902/2017 v2018" SMHI (2018).

The files are in simple comma separated table format without headers.
The Dout-t_z_lat_lon_Smod_Sobs_Tmod_Tobs.csv file contains columns with following variables [units]:
Time [matlab datenum units], Vertical coordinate [m], latitude [oN], longitude [oE], model salinity [g/kg], observed salinity [g/kg], model temperature [oC], observed temperature [oC].

The Dout-t_z_lat_lon_dS_dT_K1_K2_K3_K4_K5_K6_K7_K8_K9.csv file contains columns with following variables [units]:
4 first columns are the same as in the previous file, salinity error [g/kg], temperature error [oC], columns 7-8 are integers showing the cluster to which the error pair is designated.

do_clust_valid_DataFig.m is a Matlab script which reads the two csv files (and optionally mask file Model_mask.mat), performs the clustering analysis and creates plots which are used in Manuscript. The script is organized into %% blocks which can be executed separately (default: ctrl+enter).

k-means function is used from the Matlab Statistics and Machine Learning Toolbox.

Additional software used in the do_clust_valid_DataFig.m:

Author's auxiliary formatting scripts script/
datetick_cst.m
do_fitfig.m
do_skipticks.m
do_skipticks_y.m

Colormaps are generated using cbrewer.m (Charles, 2021).
Moving average smoothing is performed using nanmoving_average.m (Aguilera, 2021).

Additional file 1. Emory cohort case information, demographics, CERAD scores, pathology diagnosis, Reagan scores, post-mortem interval, CERAD score, Braak stage, Thal stage, ABC score.

We aim to validate machine learning methods for accurate identification of in vitro research and the subsequent assessment of risk of bias reporting of these papers. In doing so, we will address the research question: Has the reporting of risk of bias measures relevant to in vitro experiments improved over time?

This upload provides Open Data associated with the publication "Independent validation of a machine learning tool for predicting mediastinal lymph node metastases in non‑small cell lung cancer using routinely obtainable [18F]FDG‑PET/CT parameters" by Rogasch JMM et al. (2025).

The upload contains the anonymized dataset with 10 features necessary to run the final GBM model that was validated in the publication.

The dataset publication for the previous publication on the training of the model can be found here: https://doi.org/10.5281/zenodo.7094286.

Besides the dataset, this upload provides the original python scripts that were used as well as their output.

A description of all files can be found in "content_description_2025_09_13.txt".

it will make important errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques for a previous classification and mitigation of the propagation effects.

This dataset describes the training, validation and test dataset used for the development of a hybrid ANN coagulation model.

This data includes all datasets and codes for adversarial validation in geospatial machine learning prediction and corresponding experiments. Except for datasets (Brazil Amazon basion AGB dataset and synthetic species abundance dataset) and code, Reademe.txt explains each file's meaning.

Model Validation Platform Market Outlook

According to our latest research, the global Model Validation Platform market size reached USD 2.15 billion in 2024, reflecting a robust expansion driven by the increasing adoption of advanced analytics and artificial intelligence across multiple sectors. The market is projected to grow at a CAGR of 15.7% during the forecast period, reaching approximately USD 6.13 billion by 2033. This impressive growth trajectory is primarily fueled by the rising regulatory demands, the need for robust risk management frameworks, and the proliferation of machine learning models in critical business processes.

One of the principal growth factors for the Model Validation Platform market is the surge in regulatory requirements across industries such as banking, financial services, and insurance (BFSI), healthcare, and government. Regulatory bodies are mandating stringent model risk management practices to ensure the reliability and fairness of predictive models, particularly those influencing credit decisions, insurance underwriting, and patient outcomes. As a result, organizations are increasingly investing in comprehensive model validation solutions to meet compliance standards, minimize operational risks, and avoid costly penalties. The growing sophistication of models, especially with the integration of artificial intelligence and machine learning, further amplifies the need for platforms that can systematically validate, monitor, and document model performance and integrity.

The rapid digital transformation and the widespread adoption of data-driven decision-making are also catalyzing the demand for model validation platforms. Enterprises across sectors are leveraging predictive analytics, risk scoring, and automated decision systems to gain a competitive edge. However, the complexity of these models introduces risks related to model drift, bias, and operational inefficiencies. Model validation platforms play a crucial role in providing transparency, traceability, and continuous monitoring of models, thereby enabling organizations to maintain high standards of accuracy, fairness, and reliability. The increasing frequency of high-profile incidents involving model failures and algorithmic bias has heightened awareness about the importance of robust validation frameworks, further driving market growth.

Another significant growth driver is the evolution of cloud computing and the proliferation of scalable, flexible deployment options. Cloud-based model validation platforms offer organizations the agility to validate, monitor, and manage models at scale, regardless of geographical boundaries. This is especially valuable for multinational corporations and distributed teams that require centralized oversight and real-time collaboration. The ability to integrate with diverse data sources, leverage advanced analytics, and automate validation workflows positions cloud-based solutions as a preferred choice for large enterprises and small-to-medium-sized businesses alike. The ongoing advancements in cloud security, interoperability, and compliance features are expected to further accelerate the adoption of model validation platforms over the forecast period.

In the context of ensuring the reliability and integrity of predictive models, Model Robustness Testing has emerged as a critical component of the model validation process. This testing is essential to evaluate how models perform under various conditions and to identify potential vulnerabilities that could affect their accuracy and fairness. By simulating different scenarios and stress-testing models, organizations can gain insights into their resilience and adaptability. This process not only helps in mitigating risks associated with model failures but also enhances the confidence of stakeholders in the predictive capabilities of these models. As the complexity of models continues to increase, the role of robustness testing becomes even more pivotal in maintaining high standards of model performance and compliance.

From a regional perspective, North America continues to dominate the Model Validation Platform market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is underpinned by the concentration of major financial institutions, technology innovators, and stringent regulatory frameworks. Meanwhile, Asia Pacific is emerging as the fastes