- Grand Challenge

Francesco Ciompi

f.ciompi

Netherlands
Radboud University Medical Center
Pathology
Website

Organizations

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Member for 8 years, 1 month
30 challenge submissions
284 algorithms run

Activity Overview

Patch Camelyon

Reader Study Editor

Indicate which patches are malignant.

ICIAR 2018

Challenge User

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

DRIVE

Challenge User

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

ANHIR

Challenge User

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

Challenge User

Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology

Decathlon

Challenge User

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

LYON19

Challenge Editor

Automatic Lymphocyte detection in IHC stained specimens.

PAIP 2019

Challenge User

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

PatchCamelyon

Challenge Editor

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

Challenge User

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

DigestPath2019

Challenge User

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

Lymphocyte Assessment Hackathon

Challenge Editor

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

HEROHE

Challenge User

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

Challenge User

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

Challenge User

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

NuCLS

Challenge User

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

Challenge User

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

Challenge User

NODE21: generate and detect nodules on chest radiographs

WSSS4LUAD

Challenge User

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

Challenge User

Mitosis Domain Generalization Challenge 2021 (part of MICCAI 2021)

TIGER

Challenge Editor

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

CoNIC 2022

Challenge User

Colon Nuclei Identification and Counting Challenge 2022

BCNB

Challenge User

Early Breast Cancer Core-Needle Biopsy WSI Dataset

ACROBAT 2023

Challenge User

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

Challenge User

Endometrial Carcinoma Detection in Pipelle biopsies

Challenge Editor

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.