Patch Camelyon

Indicate which patches are malignant.

ICIAR 2018

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

DRIVE

Develop a system to automatically segment vessels in human retina fundus 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.

ACDC-LungHP

Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology

Decathlon

The Medical Segmentation Decathlon challenge tests the generalisability of machine learning algorithms when applied to 10 different semantic segmentation task.

LYON19

Automatic Lymphocyte detection in IHC stained specimens.

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.

VerSe`19

Vertebrae labelling and segmentation on a spine dataset on an unprecedented 150 CT scans with voxel-level vertebral annotations.

DigestPath2019

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

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.

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.

RibFrac

Rib Fracture Detection and Classification Challenge: A large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80Gb)

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

NODE21

NODE21: generate and detect nodules on chest radiographs

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

CoNIC 2022

Colon Nuclei Identification and Counting Challenge 2022

BCNB

Early Breast Cancer Core-Needle Biopsy WSI Dataset

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.

MItosis DOmain Generalization Challenge 2022

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.

OCELOT 2023: Cell Detection from Cell-Tissue Interaction

Tumor Detection in Lymph Nodes

Tissue-Background Segmentation

Gleason Grading of Prostate Biopsies

Automated Gleason grading of prostate biopsies following the Gleason Grade Group system.

HookNet-Breast

Segmentation algorithm for histopathology breast tissue.

Lung Cancer Segmentation

Lung cancer segmentation in H&E stained histopathological images.

HookNet-Lung

Segmentation algorithm for histopathology lung tissue.

Neural Image Compression

Compresses whole slide images into much smaller volumes

Quality assessment of whole-slide images through artifact detection

Quality scoring with artifact detection in whole slide images; out-of-focus, tissue folds, ink, dust, pen mark, and air bubbles.

Colon Tissue segmentation

Tissue segmentation network for colon histopathology images

Nuclear Pleomorphism Scoring

Scoring nuclear pleomorphism grade in whole-slide breast histopathology images

Breast Cancer Segmentation and Scoring in H&E

Tiger Algorithm Example

example on how to create an algorithm for the tiger challenge

Endometrial Carcinoma classification

CLAM model that computes the probability of the WSI being (pre)malignant and also outputs an interpretable heatmap.

Colon Budding in IHC

Automatic tumor bud detection in IHC stained slides of CRC

HookNet-TLS

A model for detection of TLS and GC in histopathology.