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

by   Yuhao Mo, et al.

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free 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 learning-based 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.


page 1

page 2

page 3

page 4

page 5

page 7

page 9


Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound

Breast cancer is one of the leading causes of cancer deaths in women. As...

Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosis

The heterogeneity of breast cancer presents considerable challenges for ...

Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features

Image classification is widely used to build predictive models for breas...

Vision Transformer for Classification of Breast Ultrasound Images

Medical ultrasound (US) imaging has become a prominent modality for brea...

MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis

With an aging and growing population, the number of women requiring eith...

Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning

We explore the potential of CNN-based models for gallbladder cancer (GBC...

Please sign up or login with your details

Forgot password? Click here to reset