Hierarchical Pretrained Backbone Vision Transformer for Image Classification in Histopathology
Zedda L.
;Loddo A.
;Di Ruberto C.
2023-01-01
Abstract
Histopathology plays a crucial role in clinical diagnosis, treatment planning, and research by enabling the examination of diseases in tissues and organs. However, the manual analysis of histopathological images is time-consuming and labor-intensive, requiring expert pathologists. To address this issue, this work proposes a novel architecture called Hierarchical Pretrained Backbone Vision Transformer for automated histopathological image classification, a critical tool in clinical diagnosis, treatment planning, and research. Current deep learning-based methods for image classification require a large amount of labeled data and significant computational resources to be trained effectively. By leveraging pretrained Visual Transformer backbones, our approach can classify histopathology images, achieve state-of-the-art performance, and take advantage of the pretrained backbones’ weights. We evaluated it on the Chaoyang histopathology dataset, comparing it with other state-of-the-art Visual Transformers. The experimental results demonstrate that the proposed architecture outperforms the others, indicating its potential to be an effective tool for histopathology image classification.File | Size | Format | |
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ICIAP2023__HPB_ViT_OPEN.pdf Open Access from 08/09/2024
Description: Articolo completo
Type: versione post-print
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756.3 kB | Adobe PDF | View/Open |
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