CAncer MEtastases in LYmph nOdes challeNge (CAMELYON) Dataset

Tools & Applications

• ASAP Viewer by Geert Litjens
  
• CAMELYON Challenge by Diagnostic Image Analysis Group, Radboudumc, Nijmegen
  

Publications

• 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset by Geert Litjens, et al.
  
• Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer by Babak Ehteshami Bejnordi, et al.
  
• From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge by Peter Bandi, et al.
  

MONKEY

Tools & Applications

• ASAP Viewer by Geert Litjens
  
• MONKEY Challenge by Computational Pathology Group Radboudumc, Nijmegen
  
• MONKEY challenge tutorials by Computational Pathology Group Radboudumc, Nijmegen
  
• Structured description of the MONKEY Challenge by Linda Studer, Dominique van Midden, Fazael Ayatollahi, Jeroen van der Laak
  
• Whole slide data - python package for working with wholeslide images and annotations files by Diagnostic Image Analysis Group, Radboudumc, Nijmegen
  

International Skin Imaging Collaboration (ISIC) Archive

Tutorials

• ISIC Archive Data Dictionary by International Skin Imaging Collaboration (ISIC)
  

Tools & Applications

• ISIC Archive Gallery by International Skin Imaging Collaboration (ISIC)
  
• isic-cli - The official command line tool for interacting with the ISIC Archive by International Skin Imaging Collaboration (ISIC)
  

Publications

• A patient-centric dataset of images and metadata for identifying melanomas using clinical context by Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, et al
  
• Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration by Navarrete-Dechent C, Liopyris K, Molenda M, Braun R, Curiel-Lewandrowski C, Dusza S, et al
  
• International Skin Imaging Collaboration - Designated Diagnoses (ISIC-DX): Consensus terminology for lesion diagnostic labeling by Scope A, Liopyris K, Weber J, Barnhill R, Braun R, Curiel-Lewandrowski C, et al
  
• The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection by Kurtansky N, D'Alessandro B, Gillis M, Betz-Stablein B, Cerminara S, Garcia R, et al
  

STOIC2021 Training

Tools & Applications

• STOIC2021 Challenge by Diagnostic Image Analysis Group, Radboudumc, Nijmegen
  

Publications

• How Well Do Self-Supervised Models Transfer to Medical Imaging? by Anton J, Castelli L, Chan MF, Outthers M, Tang WH, Cheung V, et al.
  
• Study of Thoracic CT in COVID-19: The STOIC Project by Revel MP, Boussouar S, de Margerie-Mellon C, Saab I, Lapotre T, Mompoint D, et al.
  

LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge (LEOPARD) Dataset

Tools & Applications

• ASAP Viewer by Geert Litjens
  
• LEOPARD Challenge by Diagnostic Image Analysis Group, Radboudumc, Nijmegen
  

TIGER Training

Tools & Applications

• TIGER Challenge by Diagnostic Image Analysis Group, Radboudumc, Nijmegen
  
• Whole slide data - python package for working with wholeslide images and annotations files by Diagnostic Image Analysis Group, Radboudumc, Nijmegen
  

CHIMERA

Tools & Applications

• CHIMERA Challenge by Computational Pathology Group Radboudumc, Nijmegen