no code implementations • 5 Dec 2023 • Zikang Xu, Fenghe Tang, Quan Quan, Jianrui Ding, Chunping Ning, S. Kevin Zhou
With the rapid expansion of machine learning and deep learning (DL), researchers are increasingly employing learning-based algorithms to alleviate diagnostic challenges across diverse medical tasks and applications.
2 code implementations • 2 Aug 2023 • Fenghe Tang, Jianrui Ding, Lingtao Wang, Chunping Ning, S. Kevin Zhou
In order to extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient fully convolutional lightweight medical image segmentation network, which enables fast and accurate auxiliary diagnosis in real scene scenarios.
1 code implementation • 24 May 2023 • Lingtao Wang, Jianrui Ding, Fenghe Tang, Chunping Ning
Accurate detection of thyroid lesions is a critical aspect of computer-aided diagnosis.
1 code implementation • 16 May 2023 • Fenghe Tang, Jianrui Ding, Lingtao Wang, Min Xian, Chunping Ning
Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
2 code implementations • 24 Oct 2022 • Fenghe Tang, Lingtao Wang, Chunping Ning, Min Xian, Jianrui Ding
However, due to the inherent local characteristics of ordinary convolution operations, U-Net encoder cannot effectively extract global context information.
no code implementations • 13 Jan 2022 • Jiaqiao Shi, Aleksandar Vakanski, Min Xian, Jianrui Ding, Chunping Ning
The accuracy, sensitivity, and specificity of tumor classification is 88. 6%, 94. 1%, and 85. 3%, respectively.
no code implementations • 23 Oct 2019 • Fei Xu, Yingtao Zhang, Min Xian, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers.
no code implementations • 18 Jun 2019 • Fei Xu, Yingtao Zhang, Min Xian, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues.
no code implementations • 27 Jun 2018 • Fei Xu, Min Xian, Yingtao Zhang, Kuan Huang, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
Automatic tumor segmentation of breast ultrasound (BUS) image is quite challenging due to the complicated anatomic structure of breast and poor image quality.
1 code implementation • 9 Jan 2018 • Min Xian, Yingtao Zhang, H. D. Cheng, Fei Xu, Kuan Huang, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems.