{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:32Z","timestamp":1761176132372,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686318"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Vision-Language Foundation Models (VLFMs) demonstrate promise in zero-shot learning through joint visual-textual representations. However, in histopathology image analysis, their effectiveness is limited by weak image-text alignment due to coarse-grained textual descriptions that fail to capture critical fine-grained visual details. This misalignment introduces semantic noise and imprecision in zero-shot retrieval, hindering the identification of relevant cases and degrading downstream classification. To address this, we introduce Retrieval-based De-noising Causal Language Modelling (RDCLM), a novel framework that refines noisy retrieval outputs from pathology VLFMs. RDCLM constructs a pathology-specific knowledge base of fine-grained, discriminative tumour malignancy descriptions using a large language model (LLM). Given a query histopathology image, a pathology VLFM retrieves candidate descriptions from this knowledge base. Our de-noising module, leveraging a frozen language model, integrates visual features with these retrieved texts, filtering irrelevant content and enhancing semantic alignment. This significantly improves retrieval precision (by an average of 10% across datasets) and enables more accurate zero-shot image classification. To further bolster performance and generalization, we propose two retrieval augmentation strategies: Retrieval Negatives Replacement (RNR) and Description-wise Shuffling (DS). Extensive evaluations across four histopathology cancer datasets demonstrate that RDCLM significantly outperforms state-of-the-art methods in both zero-shot image-text retrieval and malignancy classification, achieving an average improvement of 12.7% in F1-score and 9.6% in accuracy over the second-best competitor. These results highlight the importance of retrieval de-noising for advancing VLFM-based zero-shot learning in histopathology. Our code available at: https:\/\/github.com\/xw18958\/RDCLM<\/jats:p>","DOI":"10.3233\/faia250860","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:44:16Z","timestamp":1761126256000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Zero-Shot Learning of Pathology Vision-Language Foundation Models in Tumour Malignancy Recognition"],"prefix":"10.3233","author":[{"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, The University of Sydney"}]},{"given":"Usman","family":"Naseem","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University"}]},{"given":"Jinman","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:44:16Z","timestamp":1761126256000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250860","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}