HistoPerm: A permutation-based view generation approach for improving histopathologic feature representation learning.
| Autor: | DiPalma J; Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA., Torresani L; Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA., Hassanpour S; Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.; Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA.; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA. |
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| Jazyk: | angličtina |
| Zdroj: | Journal of pathology informatics [J Pathol Inform] 2023 Jul 04; Vol. 14, pp. 100320. Date of Electronic Publication: 2023 Jul 04 (Print Publication: 2023). |
| DOI: | 10.1016/j.jpi.2023.100320 |
| Abstrakt: | Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a view generation method for representation learning using joint embedding architectures that enhances representation learning for histology images. HistoPerm permutes augmented views of patches extracted from whole-slide histology images to improve classification performance. We evaluated the effectiveness of HistoPerm on 2 histology image datasets for Celiac disease and Renal Cell Carcinoma, using 3 widely used joint embedding architecture-based representation learning methods: BYOL, SimCLR, and VICReg. Our results show that HistoPerm consistently improves patch- and slide-level classification performance in terms of accuracy, F1-score, and AUC. Specifically, for patch-level classification accuracy on the Celiac disease dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal Cell Carcinoma dataset, patch-level classification accuracy is increased by 2% for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease dataset, models with HistoPerm outperform the fully supervised baseline model by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for the models up to 10% relative to the fully supervised baseline. These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully supervised methods. Competing Interests: None declared. (© 2023 The Authors.) |
| Databáze: | MEDLINE |
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