Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available. However, manual annotation of fine-grained colorectal tissue samples of multiple classes, especially the rare ones like stromal tumor and anal cancer is laborious and expensive. To address this, we propose a knowledge distillation-based approach, named KD-CTCNet, that effectively captures local texture information from few tissue samples, through a distillation loss, to improve the standard CNN features. The resulting enriched feature representation achieves improved classification performance specifically in low data regimes. Extensive experiments on two public datasets of colorectal tissues reveal the merits of the proposed contributions, with a consistent gain achieved over different approaches across low data settings. The code and models are publicly available on GitHub.
First Page
357
Last Page
366
DOI
10.1007/978-3-031-45676-3_36
Publication Date
10-15-2023
Keywords
Colorectal Tissue Classification, Low Data Regimes
Recommended Citation
D. Demidov et al., "Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14349 LNCS, pp. 357 - 366, Oct 2023.
The definitive version is available at https://doi.org/10.1007/978-3-031-45676-3_36
Comments
IR conditions: non-described