{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9"],"submitter":["Chen WM"],"pubmed_abstract":["<h4>Background and aims</h4>We aim to develop a diagnostic tool for pathological-image classification using transfer learning that can be applied to diverse tumor types.<h4>Methods</h4>Microscopic images of liver tissue with and without hepatocellular carcinoma (HCC) were used to train and validate the classification framework based on a convolutional neural network. To evaluate the universal classification performance of the artificial intelligence (AI) framework, histological images from colorectal tissue and the breast were collected. Images for the training and validation sets were obtained from the Xiamen Hospital of Traditional Chinese Medicine, and those for the test set were collected from Zhongshan Hospital Xiamen University. The accuracy, sensitivity, and specificity values for t"],"journal":["Frontiers in medicine"],"pagination":["853261"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9072864"],"repository":["biostudies-literature"],"pubmed_title":["Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond."],"pmcid":["PMC9072864"],"pubmed_authors":["Zheng QZ","Pan JS","Chen WM","Lin S","Dong X","Huang J","Hong MZ","Lin MJ","Xing QQ","Zhou F","Fu M","Zhang CJ"],"additional_accession":[]},"is_claimable":false,"name":"Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond.","description":"<h4>Background and aims</h4>We aim to develop a diagnostic tool for pathological-image classification using transfer learning that can be applied to diverse tumor types.<h4>Methods</h4>Microscopic images of liver tissue with and without hepatocellular carcinoma (HCC) were used to train and validate the classification framework based on a convolutional neural network. To evaluate the universal classification performance of the artificial intelligence (AI) framework, histological images from colorectal tissue and the breast were collected. Images for the training and validation sets were obtained from the Xiamen Hospital of Traditional Chinese Medicine, and those for the test set were collected from Zhongshan Hospital Xiamen University. The accuracy, sensitivity, and specificity values for t","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-19T20:10:41.848Z","creation":"2025-04-19T20:10:41.848Z"},"accession":"S-EPMC9072864","cross_references":{"pubmed":["35530044"],"doi":["10.3389/fmed.2022.853261"]}}