Search Results for author: Jiansheng Fang

Found 6 papers, 5 papers with code

Weighing Features of Lung and Heart Regions for Thoracic Disease Classification

1 code implementation26 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.

Binarization Thoracic Disease Classification

Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval

1 code implementation19 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.

Deep Triplet Hashing Network for Case-based Medical Image Retrieval

1 code implementation29 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.

General Classification Medical Image Retrieval

Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey

no code implementations9 Dec 2020 Xiaoqing Zhang, Yan Hu, Jiansheng Fang, Zunjie Xiao, Risa Higashita, Jiang Liu

This paper provides a comprehensive survey of recent advances in machine learning for cataract classification and grading based on ophthalmic images.

General Classification

Attention-based Saliency Hashing for Ophthalmic Image Retrieval

1 code implementation7 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.

Image Retrieval

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

1 code implementation7 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.

Bayesian Inference Collaborative Filtering +1

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