DRIVE

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

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

Universal Lesion Segmentation Challenge '23

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

Diminished Reality for Emerging Applications in Medicine

The Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge seeks to pioneer the integration of Diminished Reality (DR) into oral and maxillofacial surgery. While Augmented Reality (AR) has been extensively explored in medicine, DR remains largely uncharted territory. DR involves virtually removing real objects from the environment by replacing them with their background. Recent inpainting methods present an opportunity for real-time DR applications without scene knowledge. DREAMING focuses on implementing such methods to fill obscured regions in surgery scenes with realistic backgrounds, emphasizing the complex facial anatomy and patient diversity. The challenge provides a dataset of synthetic yet photorealistic surgery scenes featuring humans, simulating an operating room setting. Participants are tasked with developing algorithms that seamlessly remove disruptions caused by medical instruments and hands, offering surgeons an unimpeded view of the operative site.

ToothFairy2: Multi-Structure Segmentation in CBCT Volumes

This is the second edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Radboud University Medical Center. The challenge is hosted by grand-challenge and is part of MICCAI2024.

Pelvic Bone Fragments with Injuries Segmentation Challenge

Pelvic fracture segmentation in CT and X-ray

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.