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.
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 | Dimensione | Formato | |
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ICIAP2023__HPB_ViT_OPEN.pdf
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