no code implementations • 1 Mar 2024 • Zexin Feng, Na Zeng, Jiansheng Fang, Xingyue Wang, Xiaoxi Lu, Heng Meng, Jiang Liu
Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP).
no code implementations • 23 Jun 2022 • Jiansheng Fang, Anwei Li, Pu-Yun OuYang, Jiajian Li, Jingwen Wang, Hongbo Liu, Fang-Yun Xie, Jiang Liu
We design a deep multimodal survival network (MSN) with two feature extractors to learn discriminative features from multimodal data.
1 code implementation • 7 Jun 2022 • Jiansheng Fang, Jingwen Wang, Anwei Li, Yuguang Yan, Yonghe Hou, Chao Song, Hongbo Liu, Jiang Liu
In the management of lung nodules, we are desirable to predict nodule evolution in terms of its diameter variation on Computed Tomography (CT) scans and then provide a follow-up recommendation according to the predicted result of the growing trend of the nodule.
no code implementations • 26 May 2021 • Jiansheng Fang, Yanwu Xu, Yitian Zhao, Yuguang Yan, Junling Liu, Jiang Liu
By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.
1 code implementation • 19 May 2021 • Jiansheng Fang, Huazhu Fu, Dan Zeng, Xiao Yan, Yuguang Yan, Jiang Liu
When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database.
1 code implementation • 29 Jan 2021 • Jiansheng Fang, Huazhu Fu, Jiang Liu
The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes.
no code implementations • 9 Dec 2020 • Xiaoqing Zhang, Yan Hu, Zunjie Xiao, Jiansheng Fang, Risa Higashita, Jiang Liu
This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images.
1 code implementation • 7 Dec 2020 • Jiansheng Fang, Xiaoqing Zhang, Yan Hu, Yanwu Xu, Ming Yang, Jiang Liu
Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.
1 code implementation • 7 Dec 2020 • Jiansheng Fang, Yanwu Xu, Xiaoqing Zhang, Yan Hu, Jiang Liu
The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions.