{"url":"/dataset/nachos-dataset-oct-and-xray","name":"NACHOS dataset: OCT and Xray","full_name":null,"description_markdown":"The following datasets: \r\n\r\nKidney OCT dataset\r\n\r\nThree type of tissues were sampled: cortex, medulla, and pelvis. Image size: 185*210 pixels. The OCT dataset comes from 10 porcine kidneys. For each kidney and tissue, there are 30 volumes. Each volume contain 20 images. Total number of images: 10*3*30*20 = 18,000. The same dataset was partitioned in three different levels: image (folder: split_random), volumen (folder: split_volume), and subject (folder: split_subject).\r\n\r\nChest X-ray repository\r\n\r\nA chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. The chest X-ray repository was partitioned into four folds using three different partitioning levels. In image-level partitioning(folder: split1_random), images were randomly distributed across four folds. In patient-level partitioning (folder: split2_patient), all images from the same patient were assigned to the same fold. Finally, in dataset-level partitioning (folder: split3_dataset), each dataset was exclusively allocated to a separate fold.","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.14847200","introduced_date":"2025-03-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/integration-of-nested-cross-validation","title":"Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models","first_author":"Paul Calle","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["NACHOS dataset: OCT and Xray"],"data_loaders":[],"num_papers_in_archive":1,"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."}