Accurate subtyping of lung cancers by modelling class dependencies

Conference item

Identifying subtypes and histological patterns is crucial for lung cancer diagnosis and treatment. Nevertheless, datasets with complete subtyping annotations are scarce, and most existing work primarily focuses on categorising lung cancers into fundamental types, omitting the distinction of adenocarcinoma patterns. We present a computational approach for a more comprehensive lung cancer subtyping from histology by modelling the dependencies between cancer subtypes and histological patterns in a multi-label setting. Our approach utilises slide-level labels indicating cancer subtypes as well as the presence of cancerassociated patterns, thereby alleviating the need for labourintensive region-based annotations. A new dataset with cancer-associated pattern labels is constructed and combined with publicly available datasets. We evaluate our model’s ability to simultaneously differentiate cancer subtypes and cancer-associated patterns. The result demonstrates that our modules enable conventional weakly-supervised classification models on multi-label problems, achieving subset accuracy of 84% when differentiating lung cancer subtypes and cancer-associated histological patterns.

Authors: Batchkala, G; Li, B; Fan, M; McCole, M; Brambilla, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: 2024 IEEE International Symposium on Biomedical Imaging (ISBI)
• Pages: 1-5
• Publication date: 2024-08-22
• Acceptance date: 2024-02-09
• Event title: 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)
• Event location: Athens, Greece
• Event website: https://biomedicalimaging.org/2024/
• Event start date: 2024-05-27
• Event end date: 2024-05-30
• DOI: 10.1109/ISBI56570.2024.10635232
• EISSN: 1945-8452
• ISSN: 1945-7928
• Language: English
• Keywords: computational pathology; lung cancer; multi-label classification; multiple-instance learning