{"doi":"10.48550/arxiv.2409.03080","title":"Explainable AI for computational pathology identifies model limitations and tissue biomarkers","abstract":"Introduction: Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder clinical adoption. Explainable AI methods are essential to enhance model transparency and reliability. Methods: mutation classification in gliomas. In computational experiments, HIPPO was compared against traditional metrics and attention-based approaches to assess its ability to identify key tissue elements driving model predictions. Results: mutation classification, HIPPO more robustly identified the pathology regions responsible for false negatives compared to attention, suggesting its potential to outperform attention in explaining model decisions. Conclusions: HIPPO expands the explainable AI toolkit for computational pathology by enabling deeper insights into model behavior. This framework supports the trustworthy development, deployment, and regulation of weakly-supervised models in clinical and research settings, promoting their broader adoption in digital pathology.","journal":"PubMed","year":2024,"id":491839,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9587,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1337746,"name":"Kim, Chanwoo","orcid":null,"position":1,"is_corresponding":false},{"id":1337747,"name":"Gadgil, Soham","orcid":null,"position":2,"is_corresponding":false},{"id":1337748,"name":"Savant, Deepika","orcid":null,"position":3,"is_corresponding":false},{"id":1337749,"name":"Zhao, Zhen","orcid":null,"position":4,"is_corresponding":false},{"id":1337750,"name":"Saltz, Joel H.","orcid":null,"position":5,"is_corresponding":false},{"id":1337751,"name":"Lee, Su-In","orcid":null,"position":6,"is_corresponding":false},{"id":1337752,"name":"Koo, Peter K.","orcid":null,"position":7,"is_corresponding":false},{"id":559394,"name":"Jakub Kaczmarzyk","orcid":"0000-0002-5544-7577","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:08:45.247225Z","pmid":"39279830","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}