1, CrossMoDa, held at MICCAI 2021
- This challenge proposes the first medical imaging benchmark of unsupervised cross-modality Domain Adaptation approaches (from contrast-enhanced T1 to high-resolution T2).
- Reference: J. Shapey et al., Segmentation of vestibular schwannoma from MRI — An open annotated dataset and baseline algorithm, Scientific Data, 2021.
1, The M&Ms Challenge
- Reference: V. Campello et al., Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge, IEEE TMI 2021.
2, PolypGen, held at ISBI 2021.
- This challenge aims to addressing generalisability in polyp detection and segmentation. The data were from 6 different centres worldwide with 5 datasets types that include: i) multi-centre train-test split from 5 centres ii) polyp size-based split (participants should do this by themselves if of interest), iii) data centre wise split, iv) modality split (only test) and v) one hidden centre test.
- Reference: S. Ali et al., PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment, arXiv, 2021.
3, MIDOG microscopy domain generalization challenge, held at MICCAI 2021.
- The task is to detect mitotic figures (cells undergoing cell division) from histopathology images (object detection).
- Images were scanned by 4 different scanners, 3 out of which are labeled. In total the set consists of 200 cases of breast cancer.
4, Prostate, collected by Liu et al.
- A well-organized multi-site dataset for prostate MRI segmentation, which contains prostate T2-weighted MRI data (with segmentation mask) collected from six different data sources out of three public datasets.
5, Fundus, collected by Wang et al.