15 datasets found
  1. P

    CoNSeP Dataset

    • paperswithcode.com
    Updated Sep 13, 2023
    + more versions
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    Simon Graham; Quoc Dang Vu; Shan E Ahmed Raza; Ayesha Azam; Yee Wah Tsang; Jin Tae Kwak; Nasir Rajpoot (2023). CoNSeP Dataset [Dataset]. https://paperswithcode.com/dataset/consep
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    Dataset updated
    Sep 13, 2023
    Authors
    Simon Graham; Quoc Dang Vu; Shan E Ahmed Raza; Ayesha Azam; Yee Wah Tsang; Jin Tae Kwak; Nasir Rajpoot
    Description

    The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs, each belonging to an individual patient, and scanned with an Omnyx VL120 scanner within the department of pathology at University Hospitals Coventry and Warwickshire, UK.

  2. R

    Consep Dataset

    • universe.roboflow.com
    zip
    Updated Apr 29, 2023
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    Consep (2023). Consep Dataset [Dataset]. https://universe.roboflow.com/consep/consep
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    zipAvailable download formats
    Dataset updated
    Apr 29, 2023
    Dataset authored and provided by
    Consep
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Nuclei Bounding Boxes
    Description

    Consep

    ## Overview
    
    Consep is a dataset for object detection tasks - it contains Nuclei annotations for 2,624 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).
    
  3. t

    Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam (2024). Dataset:...

    • service.tib.eu
    Updated Dec 2, 2024
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    (2024). Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam (2024). Dataset: CoNSeP. https://doi.org/10.57702/mft063xl [Dataset]. https://service.tib.eu/ldmservice/dataset/consep
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    Dataset updated
    Dec 2, 2024
    Description

    CoNSeP dataset consists of 41 H&E stained image tiles, each of size 1000x1000 pixels at 40x objective magnification, with annotations of seven types of nuclei: malignant, normal, endothelial, miscellaneous, Fibroblast, Muscle, and inflammatory.

  4. f

    Details of the datasets used in our experiments.

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Huachang Li; Jing Zhong; Liyan Lin; Yanping Chen; Peng Shi (2023). Details of the datasets used in our experiments. [Dataset]. http://doi.org/10.1371/journal.pone.0286161.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Huachang Li; Jing Zhong; Liyan Lin; Yanping Chen; Peng Shi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.

  5. Global Consep buyers list and Global importers directory of Consep

    • volza.com
    csv
    Updated Jul 15, 2025
    + more versions
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    Volza FZ LLC (2025). Global Consep buyers list and Global importers directory of Consep [Dataset]. https://www.volza.com/buyers-united-states/united-states-importers-buyers-of-consep
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jul 15, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Count of exporters, Count of importers, Count of shipments, Sum of import value, 2014-01-01/2021-09-30
    Description

    23 Active Global Consep buyers list and Global Consep importers directory compiled from actual Global import shipments of Consep.

  6. f

    Ablation studies results on the MoNuSeg dataset.

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Huachang Li; Jing Zhong; Liyan Lin; Yanping Chen; Peng Shi (2023). Ablation studies results on the MoNuSeg dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0286161.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Huachang Li; Jing Zhong; Liyan Lin; Yanping Chen; Peng Shi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.

  7. D

    Replication Data for: Are nuclear masks all you need for improved...

    • dataverse.no
    • dataverse.azure.uit.no
    • +1more
    tar +2
    Updated May 30, 2025
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    Dhananjay Tomar; Dhananjay Tomar (2025). Replication Data for: Are nuclear masks all you need for improved out-of-domain generalization? A closer look at cancer classification in histopathology [Dataset]. http://doi.org/10.18710/NXPLFL
    Explore at:
    tar(1174466560), tar(837365760), tar(3484303360), tar(43612160), tar(27514880), tar(46243840), tar(984698880), tar(1561169920), tar(70819840), tar(1273661440), tar(188385280), tar(1601843200), tar(1847193600), tar(19650560), tar(33669120), tar(15654973440), tar(1079132160), tar(3907850240), tar(45854720), tar(40878080), tar(947025920), tar(1037199360), tar(35932160), tar(51599360), tar(71352320), tar(1915699200), tar(253081600), tar(477061120), tar(78991360), tar(104058880), tar(2372812800), text/comma-separated-values(30543730), tar(27279360), tar(290693120), tar(10588160), tar(734279680), tar(37201920), tar(9734359040), tar(14043402240), tar(2718259200), tar(404459520), tar(49152000), tar(70881280), tar(343623680), txt(5707), tar(434595840), tar(1895761920), tar(55910400), tar(735692800), tar(32798720), tar(1166909440), tar(1577318400), tar(37877760), tar(36075520), tar(3514470400), tar(18196480), tar(58910720), tar(34600960), tar(84858880), tar(151930880), tar(1028505600), tar(2686238720), tar(3079966720), tar(17448960), tar(1002332160), tar(1141084160), tar(1257820160), tar(900823040), tar(25896960), tar(583505920), tar(1331230720), tar(1886464000), tar(133120000), tar(111667200), tar(4133888000), tar(61112320), tar(124282880), tar(1967779840), tar(108236800), tar(858542080), tar(72622080), tar(2226851840), tar(10700800), tar(1031802880), tar(2083543040), tar(3743621120), tar(34549760), tar(112926720), tar(77803520), tar(28375040), tar(30924800), tar(1480734720), tar(1621288960), tar(1388861440), tar(28190720), tar(560640000), tar(3297228800), tar(13742080), tar(82298880), tar(5410396160), tar(147456000), tar(41902080)Available download formats
    Dataset updated
    May 30, 2025
    Dataset provided by
    DataverseNO
    Authors
    Dhananjay Tomar; Dhananjay Tomar
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset is a processed version of the CAMELYON17 dataset used in the NeurIPS 2024 paper "Are nuclear masks all you need for improved out-of-domain generalization? A closer look at cancer classification in histopathology". It consists of patches / tiles from 50 Whole Slide Images (WSIs) (10 WSIs from each of the 5 hospitals) in the CAMELYON17 dataset that have tumour segmentation available. Tiles were picked such that each hospital has equal number of tumourous and non-tumours tiles. Each tile is of size 270x270 pixels. A tile is considered tumourous if the centre region of tile (90x90 pixels in size) has at least 1 pixel that lies inside the tumour segmentation map. The dataset also contains nuclear segmentation masks for all the tiles. Masks were generated using HoVer-Net trained on the CoNSeP dataset.

  8. Global import data of Consep

    • volza.com
    csv
    Updated Sep 7, 2025
    + more versions
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    Volza FZ LLC (2025). Global import data of Consep [Dataset]. https://www.volza.com/imports-china/china-import-data-of-consep
    Explore at:
    csvAvailable download formats
    Dataset updated
    Sep 7, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Count of importers, Sum of import value, 2014-01-01/2021-09-30, Count of import shipments
    Description

    50 Global import shipment records of Consep with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

  9. f

    Pre-experiment tuning of c1 and c2.

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Huachang Li; Jing Zhong; Liyan Lin; Yanping Chen; Peng Shi (2023). Pre-experiment tuning of c1 and c2. [Dataset]. http://doi.org/10.1371/journal.pone.0286161.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Huachang Li; Jing Zhong; Liyan Lin; Yanping Chen; Peng Shi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.

  10. R

    Badminton Proof Of Concept Dataset

    • universe.roboflow.com
    zip
    Updated Nov 5, 2023
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    Frederik Wulf (2023). Badminton Proof Of Concept Dataset [Dataset]. https://universe.roboflow.com/frederik-wulf-uqesy/badminton-proof-of-concept
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    zipAvailable download formats
    Dataset updated
    Nov 5, 2023
    Dataset authored and provided by
    Frederik Wulf
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Badminton Court Polygons
    Description

    Badminton Proof Of Concept

    ## Overview
    
    Badminton Proof Of Concept is a dataset for instance segmentation tasks - it contains Badminton Court annotations for 200 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).
    
  11. s

    Consep Import Data in January - Seair.co.in

    • seair.co.in
    Updated Jan 6, 2016
    + more versions
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    Seair Exim (2016). Consep Import Data in January - Seair.co.in [Dataset]. https://www.seair.co.in
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Jan 6, 2016
    Dataset provided by
    Seair Info Solutions PVT LTD
    Authors
    Seair Exim
    Area covered
    Rwanda, Northern Mariana Islands, Greenland, Saint Barthélemy, Gibraltar, Pitcairn, French Southern Territories, Russian Federation, Heard Island and McDonald Islands, Cocos (Keeling) Islands
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

  12. Consep Import Data in June - Seair.co.in

    • seair.co.in
    Updated Jun 24, 2016
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    Seair Exim (2016). Consep Import Data in June - Seair.co.in [Dataset]. https://www.seair.co.in
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Jun 24, 2016
    Dataset provided by
    Seair Exim Solutions
    Authors
    Seair Exim
    Area covered
    Guinea-Bissau, Saudi Arabia, Australia, Iraq, Colombia, Portugal, Korea (Democratic People's Republic of), Greece, Saint Helena, Yemen
    Description

    Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

  13. Consep Sizetec Limited | See Full Import/Export Data | Eximpedia

    • eximpedia.app
    Updated Feb 14, 2025
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    Seair Exim (2025). Consep Sizetec Limited | See Full Import/Export Data | Eximpedia [Dataset]. https://www.eximpedia.app/
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 14, 2025
    Dataset provided by
    Eximpedia Export Import Trade
    Eximpedia Export Import Trade Data
    Authors
    Seair Exim
    Area covered
    Bosnia and Herzegovina, Jersey, Martinique, South Sudan, Palau, Saint Barthélemy, Northern Mariana Islands, Vietnam, Jordan, Tanzania
    Description

    Consep Sizetec Limited Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

  14. h

    photo-concept-bucket

    • huggingface.co
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    Bagheera, photo-concept-bucket [Dataset]. https://huggingface.co/datasets/bghira/photo-concept-bucket
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Authors
    Bagheera
    License

    https://choosealicense.com/licenses/openrail++/https://choosealicense.com/licenses/openrail++/

    Description

    Photo Concept Bucket

    The purpose of this dataset was to distribute a high quality, free-to-use dataset containing samples that require no attribution and have an open license. All of the images were captioned in a cluster containing:

    38x 3090 24G 6x 4090 24G 8x A5000 24G 2x A100 80G A couple volunteers running a 3090 or 4090.

    The model was running in fp8 precision using 🤗Transformers and 🤗Accelerate for easy multi-GPU captioning. The captioning was spread across 10 different… See the full description on the dataset page: https://huggingface.co/datasets/bghira/photo-concept-bucket.

  15. e

    Eximpedia Export Import Trade

    • eximpedia.app
    Updated Feb 5, 2025
    + more versions
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    Seair Exim (2025). Eximpedia Export Import Trade [Dataset]. https://www.eximpedia.app/
    Explore at:
    .bin, .xml, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Eximpedia PTE LTD
    Eximpedia Export Import Trade Data
    Authors
    Seair Exim
    Area covered
    Myanmar, Gabon, Saint Helena, Singapore, Greenland, Brazil, New Caledonia, Western Sahara, Kenya, Niue
    Description

    Access Consep import export data of global countries with importers' & exporters' details, shipment date, price, hs code, ports, quantity etc.

  16. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Simon Graham; Quoc Dang Vu; Shan E Ahmed Raza; Ayesha Azam; Yee Wah Tsang; Jin Tae Kwak; Nasir Rajpoot (2023). CoNSeP Dataset [Dataset]. https://paperswithcode.com/dataset/consep

CoNSeP Dataset

Colorectal Nuclear Segmentation and Phenotypes

Explore at:
Dataset updated
Sep 13, 2023
Authors
Simon Graham; Quoc Dang Vu; Shan E Ahmed Raza; Ayesha Azam; Yee Wah Tsang; Jin Tae Kwak; Nasir Rajpoot
Description

The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs, each belonging to an individual patient, and scanned with an Omnyx VL120 scanner within the department of pathology at University Hospitals Coventry and Warwickshire, UK.

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