EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks

Shi, Lei; Li, Xiaoyan; Hu, Weiming; Chen, Haoyuan; Chen, Jing; Zhang, Fan; Gao, Minghe; Yujie, Jing,; Lu, Guotao; Deguo, Ma,; Ma, Zhangfeng; Meng, Qingtao; Dechao, Tang,; Sun, Hongzan; Grzegorzek, Marcin; Qi, Shouliang; Teng, Yueyang; Li, Chen

doi:10.48550/arxiv.2212.00532

2022

DOI: 10.48550/arxiv.2212.00532

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EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks

Lei Shi¹,

Xiaoyan Li²,

Weiming Hu³

et al.

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“…evaluate our DDViT model on Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg)(Shi et al 2022), which is also known as Colorectal cancer. It is a prevalent and potentially life-threatening disease, remains a significant public health concern worldwide.…”

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DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)

Sun,

Sheng

2024

AAAI

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With the help of Vision transformers (ViTs), medical image segmentation was able to achieve outstanding performance. In particular, they overcome the limitation of convolutional neural networks (CNNs) which rely on local receptive fields. ViTs use self-attention mechanisms to consider relationships between all image pixels or patches simultaneously. However, they require large datasets for training and did not perform well on capturing low-level features. To that end, we propose DDViT, a novel ViT model that unites a CNN to alleviate data-hunger for medical image segmentation with two multi-scale feature representations. Significantly, our approach incorporates a ViT with a plug-in domain adapter (DA) with Double-Level Fusion (DLF) technique, complemented by a mutual knowledge distillation paradigm, facilitating the seamless exchange of knowledge between a universal network and specialized domain-specific network branches. The DLF framework plays a pivotal role in our encoder-decoder architecture, combining the innovation of the TransFuse module with a robust CNN-based encoder. Extensive experimentation across diverse medical image segmentation datasets underscores the remarkable efficacy of DDViT when compared to alternative approaches based on CNNs and Transformer-based models.

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DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)

Sun,

Sheng

2024

AAAI

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EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks

Cited by 1 publication

References 0 publications

DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)

DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)

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