{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Kapse S"],"funding":["NCI NIH HHS"],"pagination":["11226-11237"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11601081"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["2024"],"pubmed_abstract":["Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides. Traditionally, MIL interpretability is limited to identifying salient regions deemed pertinent for downstream tasks, offering little insight to the end-user (pathologist) regarding the rationale behind these selections. To address this, we propose Self-Interpretable MIL (SI-MIL), a method intrinsically designed for interpretability from the very outset. SI-MIL employs a deep MIL framework to guide an interpretable branch grounded on handcrafted pathological features, facilitating linear predictions. Beyond identifying salient regions, SI-MIL uniquely provides feature-level interpretations rooted in pathologic"],"journal":["Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition"],"pubmed_title":["SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology."],"pmcid":["PMC11601081"],"funding_grant_id":["U24 CA215109","R21 CA258493"],"pubmed_authors":["Das S","Zhang J","Samaras D","Kapse S","Pati P","Vakalopoulou M","Prasanna P","Gupta RR","Chen C","Saltz J"],"additional_accession":[]},"is_claimable":false,"name":"SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology.","description":"Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides. Traditionally, MIL interpretability is limited to identifying salient regions deemed pertinent for downstream tasks, offering little insight to the end-user (pathologist) regarding the rationale behind these selections. To address this, we propose Self-Interpretable MIL (SI-MIL), a method intrinsically designed for interpretability from the very outset. SI-MIL employs a deep MIL framework to guide an interpretable branch grounded on handcrafted pathological features, facilitating linear predictions. Beyond identifying salient regions, SI-MIL uniquely provides feature-level interpretations rooted in pathologic","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2025-04-22T14:30:40.405Z","creation":"2025-04-06T01:02:27.204Z"},"accession":"S-EPMC11601081","cross_references":{"pubmed":["39606709"],"doi":["10.1109/cvpr52733.2024.01067"]}}