{"url":"/dataset/retouch","name":"RETOUCH","full_name":"RETOUCH -The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge","description_markdown":"The goal of the challenge is to compare automated algorithms that are able to detect and segment various types of fluids on a common dataset of optical coherence tomography (OCT) volumes representing different retinal diseases, acquired with devices from different manufacturers. We made available a dataset of OCT volumes containing a wide variety of retinal fluid lesions with accompanying reference annotations. We invite the medical imaging community to participate by developing and testing existing and novel automated retinal OCT segmentation methods.","description_withheld":null,"homepage":"https://retouch.grand-challenge.org/","introduced_date":"2022-04-29","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Retinal OCT Disease Classification","url":"/task/retinal-oct-disease-classification","datasets_with_task":"/datasets/task/retinal-oct-disease-classification"}],"languages":[],"variants":["RETOUCH"],"data_loaders":[{"repo":"https://github.com/hnvwavh/test","url":"https://github.com/hnvwavh/test","frameworks":["pytorch"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}