In numerous academic papers, the number refers specifically to the subset of the NIH ChestX-ray14 dataset that contains one or more pathologies . The full dataset consists of 112,120 frontal-view images, of which 51,708 are labeled with at least one of 14 common thoracic diseases.
If your file pertains to engineering or natural sciences rather than medical imaging, it may refer to one of the following high-impact papers where "51708" is a significant article number or DOI fragment: 51708.rar
: "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases". Other Academic Matches In numerous academic papers, the number refers specifically
: "Metabolic heterogeneity of the tumor microenvironment in colorectal cancer lung metastasis", published in Nature Communications (2024), with DOI suffix 51708-9 . Other Academic Matches : "Metabolic heterogeneity of the
: "A Coupled Thermo-Mechanical Dynamic Characterization of Cylindrical Batteries" , published in IEEE Access (2022), where the page range begins at 51708 .
: "Enof-SNN: Training Accurate Spiking Neural Networks via Enhancing the Output Feature", published in NIPS/NeurIPS (2024), where the page range is 51708–51726 .
If you are looking for the original paper that introduced this dataset and its specific 51,708-image pathology distribution, it is: