Zhao, Lu and Zhao, Wangyuan and Qiu, Lu and Jiang, Mengqi and Qian, Liqiang and Ting, Hua-Nong and Fu, Xiaolong and Zhang, Puming and Han, Yuchen and Zhao, Jun (2024) CoLM: Contrastive learning and multiple instance learning network for lung cancer classification of surgical options based on frozen pathological images. Biomedical Signal Processing and Control, 100 (A). p. 107097. ISSN 1746-8094, DOI https://doi.org/10.1016/j.bspc.2024.107097.
Full text not available from this repository.Abstract
Histopathological images are regarded as the gold standard in cancer diagnosis. Formalin-fixed paraffin- embedded (FFPE) tissues are routinely collected and archived for pathological examination. However, the time-consuming procedures of tissue fixation and embedding render FFPE tissues unsuitable for intraoperative diagnosis, where immediate results are crucial during surgical procedures. In contrast, obtaining afresh frozen section (FS) takes a very short time. FS samples are widely utilized for intraoperative diagnosis, whereas the diagnostic accuracy of FS is currently limited by the presence of potential histological artifacts. In this paper, we propose a contrastive learning image translation and multiple instance learning network (CoLM) for lung cancer classification. CoLM efficiently translates FS images into FFPE-style images and facilitates whole slide image classification. The entire framework encompasses two crucial stages. In the first stage, we employ a contrastive learning translation network with a dual-attention module (CL-DAM) for image translation. In the second stage, we utilize a hybrid transformer multi-instance learning-based network (HTM) to address the challenge posed by weak labels. We conduct experiments on lung cancer datasets to validate the performance of our proposed approach. The results demonstrate that our method achieve superior classification performance over other state-of-the-art methods, effectively mitigating the impact of blurred FS images. The proposed framework not only elevates the precision of intraoperative diagnosis when employing FS but also provides valuable reference for pathologists through the application of synthetic images.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) (92059206), Shanghai Hospital Development Center, China Clinical Science and Technology Innovation project (SHDC12019X22), National Key R&D Program of China (2016YFC0104608), National Natural Science Foundation of China (NSFC) (81371634) |
| Uncontrolled Keywords: | Lung cancer; Whole slide image; Contrastive learning; Image translation; Multiple instance learning |
| Subjects: | R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Feb 2025 02:43 |
| Last Modified: | 18 Feb 2025 02:43 |
| URI: | http://eprints.um.edu.my/id/eprint/47395 |
Actions (login required)
 |
View Item |
