ISIC 2024 - Skin Cancer Detection with 3D-TBP

What should I expect the data format to be? The dataset consists of diagnostically labelled images with additional metadata. The images are JPEGs. The associated .csv file contains a binary diagnostic label (target), potential input variables (e.g. age_approx, sex, anatom_site_general, etc.), and additional attributes (e.g. image source and precise diagnosis). What am I predicting? In this challenge you are differentiating benign from malignant cases. For each image (isic_id) you are assigning the probability (target) ranging [0, 1] that the case is malignant. The SLICE-3D dataset - skin lesion image crops extracted from 3D TBP for skin cancer detection To mimic non-dermoscopic images, this competition uses standardized cropped lesion-images of lesions from 3D Total Body Photography (TBP). Vectra WB360, a 3D TBP product from Canfield Scientific, captures the complete visible cutaneous surface area in one macro-quality resolution tomographic image. An AI-based software then identifies individual lesions on a given 3D capture. This allows for the image capture and identification of all lesions on a patient, which are exported as individual 15x15 mm field-of-view cropped photos. The dataset contains every lesion from a subset of thousands of patients seen between the years 2015 and 2024 across nine institutions and three continents. The following are examples from the training set. 'Strongly-labelled tiles' are those whose labels were derived through histopathology assessment. 'Weak-labelled tiles' are those who were not biopsied and were considered 'benign' by a doctor.