Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image

Di, Xiaohui; Zhong, Shengzhou

doi:10.1016/j.cmpb.2021.106612

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Feature maps were used to classify breast tumors under semi-supervised learning and to reconstruct feature maps guided by lesion classification under unsupervised learning. Di et al [28] introduced a saliency-guided approach to differentiate the foreground and background regions by two separated branches. A hierarchical feature aggregation branch was proposed to fuse the features from both branches and make the inference.…”

Section: A Breast Cancer Diagnosis In Ultrasound Imagesmentioning

confidence: 99%

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

Han

Liu

et al. 2023

IEEE Trans. Med. Imaging

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Ultrasonography is an important routine examination for breast cancer diagnosis, due to its noninvasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer

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Section: A Breast Cancer Diagnosis In Ultrasound Imagesmentioning

confidence: 99%

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

Han

Liu

et al. 2023

IEEE Trans. Med. Imaging

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“…Cui et al [26] proposed an FMRNet to fuse combined tumoral, intratumoral and peritumoral regions to represent the whole tumor heterogeneous. Di et al [27] introduced a saliency-guided approach to differentiate the foreground and background regions by two separated branches. A hierarchical feature aggregation branch was proposed to fuse the features from both branches and make the inference.…”

Section: A Breast Cancer Diagnosis In Ultrasound Imagesmentioning

confidence: 99%

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Mo¹,

Han²,

Liu³

et al. 2022

Preprint

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Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiationfree and low-cost properties. However, it is still not the first-line screening test for breast cancer due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learningbased computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter-and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&GYFYY for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via a five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability.

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A multimodal breast cancer diagnosis method based on Knowledge-Augmented Deep Learning

Guo,

Lu,

Chen

et al. 2024

Biomedical Signal Processing and Control

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Saliency map-guided hierarchical dense feature aggregation framework for breast lesion classification using ultrasound image

Cited by 10 publications

References 25 publications

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

A multimodal breast cancer diagnosis method based on Knowledge-Augmented Deep Learning

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