CAMELYON17

Automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists.

ICIAR 2018

Can you develop a method for automatic detection of cancerous regions in breast cancer histology images?

ANHIR

The challenge focuses on comparing the accuracy (using manually annotated landmarks) and the approximate speed of automatic non-linear registration methods for aligning microscopy images of multi-stained histology tissue samples.

BreastPathQ: Cancer Cellularity Challenge 2019

SPIE-AAPM-NCI BreastPathQ:Cancer Circularity Challenge 2019: Participants will be tasked to develop an automated method for analyzing histology patches extracted from whole slide images and assign a score reflecting cancer cellularity for tumor burden assessment in each.

ACDC-LungHP

Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology

PAIP 2019

PAIP2019: Liver Cancer Segmentation Task 1: Liver Cancer Segmentation Task 2: Viable Tumor Burden Estimation

PatchCamelyon

PatchCamelyon is a new and challenging image classification dataset of 327.680 color images (96 x 96px) extracted from histopathology images of the CAMELYON16 challenge. The goal is to detect breast cancer metastasis in lymph nodes.

DigestPath2019

Welcome to Digestive-System Pathological Detection and Segmentation Challenge 2019. This competition is part of the MICCAI 2019 Challenge.

ODIR-2019

北京大学国际眼底图像智能识别竞赛 Peking University International Competition on Ocular Disease Intelligent Recognition

Lymphocyte Assessment Hackathon

Lymphocyte Assessment Hackathon in conjunction with the MICCAI COMPAY 2019 Workshop on Computational Pathology

HEROHE

Unlike previous challenges, this proposes to find an image analysis algorithm to identify HER2-positive from HER2-negative breast cancer specimens evaluating only the morphological features present on the HE slide, without the staining patterns of IHC.

EndoCV2020

Endoscopy computer vision challenge (EndoCV2020) introduces two core sub-themes in endoscopy: 1) artefact detection and segmentation (EAD2020) and 2) disease detection and segmentation (EDD2020).

PAIP2020

Built on the success of its predecessor, PAIP2020 is the second challenge organized by the Pathology AI Platform (PAIP) and the Seoul National University Hospital (SNUH). PAIP2020 will proceed to not only detect whole tumor areas in colorectal cancers but also to classify their molecular subtypes, which will lead to characterization of their heterogeneity with respect to prognoses and therapeutic responses. All participants should predict one of the molecular carcinogenesis pathways, i.e., microsatellite instability(MSI) in colorectal cancer, by performing digital image analysis without clinical tests. This task has a high clinical relevance as the currently used procedure requires an extensive microscopic assessment by pathologists. Therefore, those automated algorithms would reduce the workload of pathologists as a diagnostic assistance.

MitoEM

Large-scale 3D mitochondria instance segmentation benchmark

COVID-19 LUNG CT LESION SEGMENTATION CHALLENGE - 2020

This challenge will create the platform to evaluate emerging methods for the segmentation and quantification of lung lesions caused by SARS-CoV-2 infection from CT images.

SegPC-2021

This challenge is positioned towards robust segmentation of cells which is the first stage to build such a tool for plasma cell cancer, namely, Multiple Myeloma (MM), which is a type of blood cancer.

EndoCV2021

Endoscopy Computer Vision Challenge 2021

PAIP2021

PAIP 2021 Challenge; Perineural invasion in multiple organ cancer (colon, prostate and pancreatobiliary tract)

NuCLS

Classification, Localization and Segmentation of nuclei in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad et al. 2021. arXiv:2102.09099 [cs.CV].

Breast Cancer Segmentation

Semantic segmentation of histologic regions in scanned FFPE H&E stained slides of triple-negative breast cancer from The Cancer Genome Atlas. See: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083

WSSS4LUAD

The WSSS4LUAD dataset contains over 10,000 patches of lung adenocarcinoma from whole slide images from Guangdong Provincial People's Hospital and TCGA with image-level annotations. The goal of this challenge is to perform semantic segmentation for differentiating three important types of tissues in the WSIs of lung adenocarcinoma, including cancerous epithelial region, cancerous stroma region and normal region. Paticipants have to use image-level annotations to give pixel-level prediction.

MIDOG Challenge 2021

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 2021)

TIGER

Grand challenge on automate assessment of tumor infiltrating lymphocytes in digital pathology slides of triple negative and Her2-positive breast cancers

AIROGS

Artificial Intelligence for RObust Glaucoma Screening Challenge

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

BCNB

Early Breast Cancer Core-Needle Biopsy WSI Dataset

AGGC22

ACROBAT 2023

The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.

Cell Segmentation in Multi-modality Microscopy Images

Weakly Supervised Cell Segmentation in Multi-modality High-resolution Microscopy Images

Breast Cancer Immunohistochemical Image Generation Challenge

The Breast Cancer Immunohistochemical Image Generation Challenge aims to directly generate IHC-stained breast cancer histopathology images from HE-stained images.

PAIP 2023: TC prediction in pancreatic and colon cancer

Tumor cellularity prediction in pancreatic cancer (supervised learning) and colon cancer (transfer learning)

Endometrial Carcinoma Detection in Pipelle biopsies

Evaluation platform as reference benchmark for algorithms that can predict endometrial carcinoma on whole-slide images of Pipelle sampled endometrial slides stained in H&E, based on the test data set used in our project.

Robust Non-rigid Registration Challenge for Expansion Microscopy

SPIDER

Foundation Model Prompting for Medical Image Classification

The primary objective of this challenge is to promote the development and evaluation of model adaptation techniques for medical image classification to leverage the existing foundation models.

OCELOT 2023: Cell Detection from Cell-Tissue Interaction

CHAIMELEON Open Challenges

A unique opportunity for scientists to advance cancer research with AI. The CHAIMELEON Open Challenges invites participants to collaborate to develop and train new AI-powered solutions driving innovation in cancer diagnosis and treatment.

PANORAMA

Artificial Intelligence and Radiologists at Pancreatic Cancer Diagnosis in CT

Justified Referral in AI Glaucoma Screening

Light My Cells : Bright Field to Fluorescence Imaging Challenge

Join the Light My Cells France-Bioimaging challenge! Enhance biology and microscopy by contributing to the development of new image-to-image deep labelling methods. The task: predict the best-focused output images of several fluorescently labelled organelles from label-free transmitted light input images. Dive into the future of imaging with us! 🌐🔬 #LightMyCellsChallenge

The LEOPARD Challenge

AI4Life Microscopy Denoising Challenge

Wellcome to AI4Life-MDC24! In this challenge, we want to focus on an unsupervised denoising of microscopy images. By participating, researchers can contribute to a critical area of scientific research, aiding in interpreting microscopy images and potentially unlocking discoveries in biology and medicine.

Self-supervised learning for 3D light-sheet microscopy image seg

Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation

MONKEY challenge: Detection of inflammation in kidney biopsies

MONKEY (Machine-learning for Optimal detection of iNflammatory cells in KidnEY)

PUMA: Panoptic segmentation of nUclei and tissue in MelanomA

The PUMA Challenge aims to enhance nuclei and tissue segmentation in melanoma histopathology, addressing the need for better prognostic biomarkers to predict treatment responses. Melanoma, a highly aggressive skin cancer, often requires immune checkpoint inhibition therapy, but only half of patients respond. Prognostic biomarkers like tumor infiltrating lymphocytes (TILs) correlate with better therapy responses and lower recurrence rate, but manual TIL scoring is subjective and inconsistent. Current deep learning methods underperform. The PUMA dataset includes annotated primary and metastatic melanoma regions to improve segmentation techniques. The challenge includes two tracks with tasks focused on tissue and nuclei segmentation, encouraging advanced methods to improve predictive accuracy.

Colon Tissue segmentation

Tissue segmentation network for colon histopathology images

AIROGS Baseline

A baseline algorithm for the AIROGS challenge

Tiger Algorithm Example

example on how to create an algorithm for the tiger challenge