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 | |
---|---|---|---|
ICIAP2023__HPB_ViT_OPEN.pdf Open Access dal 08/09/2024
Descrizione: Articolo completo
Tipologia: versione post-print
Dimensione 756.3 kB
Formato Adobe PDF
|
756.3 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.