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- W4387294246 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." @default.
- W4387294246 created "2023-10-03" @default.
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- W4387294246 date "2023-09-29" @default.
- W4387294246 modified "2023-10-04" @default.
- W4387294246 title "Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study" @default.
- W4387294246 doi "https://doi.org/10.48550/arxiv.2309.17223" @default.
- W4387294246 hasPublicationYear "2023" @default.
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