13 citations · 14 across the 4 of their papers we have counts for
5 papers
A Concept-informed Interpretable Artificial Intelligence Framework for Breast Ultrasound Diagnosis
Shengzhou Zhong, Ziying Wang, Liang Li +14
Most existing artificial intelligence systems for breast ultrasound rely primarily on image-driven prediction, with limited incorporation of the knowledge from the Breast Imaging R…
TRACE: Training-time Report-guided and Clinically Ordered Concept Editing
Wentao Yue, Tianyou Lai, Jiayu Luo +4
Breast ultrasound diagnosis relies on clinically meaningful semantic concepts, yet most deep learning methods adopt end-to-end image-to-label paradigms that lack interpretability a…
DCMIL: A Progressive Representation Learning of Whole Slide Images for Cancer Prognosis Analysis
Chao Tu, Kun Huang, Jie Zhang +3
The burgeoning discipline of computational pathology shows promise in harnessing whole slide images (WSIs) to quantify morphological heterogeneity and develop objective prognostic…
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images
Zhenyuan Ning, Yixiao Mao, Qianjin Feng +2
Complex scenario of ultrasound image, in which adjacent tissues (i.e., background) share similar intensity with and even contain richer texture patterns than lesion region (i.e., f…
CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation
Zhenyuan Ning, Ke Wang, Shengzhou Zhong +2
Breast ultrasound (BUS) image segmentation plays a crucial role in a computer-aided diagnosis system, which is regarded as a useful tool to help increase the accuracy of breast can…