DFUC 2022

Diabetes is a global epidemic affecting around 425 million people and expected to rise to 629 million by 2045. Diabetic Foot Ulcer (DFU) is a severe condition that can result from the disease. The rise of the condition over the last decades is a challenge for healthcare systems. Cases of DFU usually lead to severe conditions that greatly prolongs treatment and result in limb amputation or death. Recent research focuses on creating detection algorithms to monitor their condition to improve patient care and reduce strain on healthcare systems. Work between Manchester Metropolitan University, Lancashire Teaching Hospitals and Manchester University NHS Foundation Trust has created an international repository of up to 11,000 DFU images. Analysis of ulcer regions is a key for DFU management. Delineation of ulcers is time-consuming. With effort from the lead scientists of the UK, US, India and New Zealand, this challenge promotes novel work in DFU segmentation and promote interdisciplinary researcher collaboration.

Justified Referral in AI Glaucoma Screening

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.

The LEOPARD Challenge

AutoPET III

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.

Ischemic Stroke Lesion Segmentation Challenge 2024

Pelvic Bone Fragments with Injuries Segmentation Challenge

Pelvic fracture segmentation in CT and X-ray