Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

• URL: http://arxiv.org/abs/2309.17223v1
• Date: Fri, 29 Sep 2023 13:22:17 GMT
• Title: Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study
• Authors: Vladimir Despotovic, Sang-Yoon Kim, Ann-Christin Hau, Aliaksandra Kakoichankava, Gilbert Georg Klamminger, Felix Bruno Kleine Borgmann, Katrin B. M. Frauenknecht, Michel Mittelbronnf, Petr V. Nazarov
• Abstract summary: We compare various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. A semi-supervised learning approach is proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images. The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning.
• Score: 9.161480191416551
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist's efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas; thus, providing insights to pathologists concerning the most informative parts of the WSI.

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