Search Results for author: Jun Shi

Found 22 papers, 8 papers with code

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

1 code implementation27 Jun 2022 Jun Li, Yushan Zheng, Kun Wu, Jun Shi, Fengying Xie, Zhiguo Jiang

In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis.

Contrastive Learning Representation Learning +1

Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification

1 code implementation27 Jun 2022 Yushan Zheng, Jun Li, Jun Shi, Fengying Xie, Zhiguo Jiang

Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc.

Classification Image Classification

Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images

no code implementations31 May 2022 Jun Shi, Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Jun Wang, Shihui Ying

In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models.

Contrastive Learning Federated Learning +1

A channel attention based MLP-Mixer network for motor imagery decoding with EEG

no code implementations21 Oct 2021 Yanbin He, Zhiyang Lu, Jun Wang, Jun Shi

Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task.

EEG

Causal Incremental Graph Convolution for Recommender System Retraining

1 code implementation16 Aug 2021 Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang

Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.

Causal Inference Recommendation Systems

Incremental Learning for Personalized Recommender Systems

no code implementations13 Aug 2021 Yunbo Ouyang, Jun Shi, Haichao Wei, Huiji Gao

Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment.

Incremental Learning Recommendation Systems

Logit Attenuating Weight Normalization

no code implementations12 Aug 2021 Aman Gupta, Rohan Ramanath, Jun Shi, Anika Ramachandran, Sirou Zhou, Mingzhou Zhou, S. Sathiya Keerthi

Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems.

Image Classification Recommendation Systems

Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

no code implementations29 Jun 2021 Zhiyang Lu, Zheng Li, Jun Wang, Jun Shi, Dinggang Shen

To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training.

Self-Supervised Learning

Task-driven Semantic Coding via Reinforcement Learning

no code implementations7 Jun 2021 Xin Li, Jun Shi, Zhibo Chen

However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process.

Face Detection License Plate Detection +3

DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images

1 code implementation14 May 2021 Jun Shi, Huite Yi, Shulan Ruan, Zhaohui Wang, Xiaoyu Hao, Hong An, Wei Wei

The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy.

Computed Tomography (CT)

Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions

no code implementations16 Apr 2021 Yushan Zheng, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Jun Shi, Chenghai Xue

While, it is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database that consists of histopathological whole slide images (WSIs) for a query ROI.

Graph Embedding Image Retrieval +1

SRA-LSTM: Social Relationship Attention LSTM for Human Trajectory Prediction

no code implementations31 Mar 2021 Yusheng Peng, Gaofeng Zhang, Jun Shi, Benzhu Xu, Liping Zheng

Pedestrian trajectory prediction for surveillance video is one of the important research topics in the field of computer vision and a key technology of intelligent surveillance systems.

Computer Vision Pedestrian Trajectory Prediction +1

Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography

no code implementations15 Jan 2021 Ronglin Gong, Jun Wang, Jun Shi

In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammography-based CAD.

General Classification Image Restoration +2

Reconstruction of Quantitative Susceptibility Maps from Phase of Susceptibility Weighted Imaging with Cross-Connected $Ψ$-Net

no code implementations12 Oct 2020 Zhiyang Lu, Jun Li, Zheng Li, Hongjian He, Jun Shi

In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $\Psi$-Net (C$\Psi$-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing.

Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images

no code implementations21 Jul 2020 Tianwen Zhang, Xiaoling Zhang, Jun Shi, Shunjun Wei, Jianguo Wang, Jianwei Li, Hao Su, Yue Zhou

Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1 code implementation6 Apr 2020 Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen

In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

Computed Tomography (CT)

SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration

no code implementations12 Mar 2020 Jun Shi, Jianfeng Xu, Kazuyuki Tasaka, Zhibo Chen

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications.

Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics

no code implementations16 Oct 2019 Jun Shi, Zhibo Chen

Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc.

General Classification Quantization

Lightweight Image Super-Resolution with Adaptive Weighted Learning Network

1 code implementation4 Apr 2019 Chaofeng Wang, Zheng Li, Jun Shi

PyTorch code for our paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network"

Image Super-Resolution

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