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MIST: Multi-instance selective transformer for histopathological subtype prediction
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DOI：10.1016/j.media.2024.103251.png)
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摘要
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Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate histopathological subtype prediction is a challenging task due to (1) instance -level discrimination of histopathological images, (2) low inter -class and large intraclass variances among histopathological images in their shape and chromatin texture, and (3) heterogeneous feature distribution over different images. In this paper, we formulate subtype prediction as fine-grained representation learning and propose a novel multi -instance selective transformer (MIST) framework, effectively achieving accurate histopathological subtype prediction. The proposed MIST designs an effective selective selfattention mechanism with multi -instance learning (MIL) and vision transformer (ViT) to adaptive identify informative instances for fine-grained representation. Innovatively, the MIST entrusts each instance with different contributions to the bag representation based on its interactions with instances and bags. Specifically, a SiT module with selective multi -head self -attention (S-MSA) is well -designed to identify the representative instances by modeling the instance -to -instance interactions. On the contrary, a MIFD module with the information bottleneck is proposed to learn the discriminative fine-grained representation for histopathological images by modeling instance -to -bag interactions with the selected instances. Substantial experiments on five clinical benchmarks demonstrate that the MIST achieves accurate histopathological subtype prediction and obtains state-of-the-art performance with an accuracy of 0.936. The MIST shows great potential to handle fine-grained medical image analysis, such as histopathological subtype prediction in clinical applications.
Keyword:
Multi-instance learning
Histopathological subtype prediction
Self-attention
Feature decoupling
Information bottleneck
期刊
IF:
11.8
论文数:
3.9K
被引数:
2.4W
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