Search Results for author: Zhenghua Xu

Found 25 papers, 6 papers with code

C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network

no code implementations9 Oct 2023 Ruizhi Wang, Xiangtao Wang, Jie zhou, Thomas Lukasiewicz, Zhenghua Xu

In addition, word-level optimization based on numbers ignores the semantics of reports and medical images, and the generated reports often cannot achieve good performance.

Contrastive Learning Medical Report Generation

AMLP:Adaptive Masking Lesion Patches for Self-supervised Medical Image Segmentation

no code implementations8 Sep 2023 Xiangtao Wang, Ruizhi Wang, Jie zhou, Thomas Lukasiewicz, Zhenghua Xu

The proposed strategies effectively address limitations in applying masked modeling to medical images, tailored to capturing fine lesion details vital for segmentation tasks.

Image Segmentation Medical Image Segmentation +3

MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation

no code implementations15 Apr 2023 Ruizhi Wang, Xiangtao Wang, Zhenghua Xu, Wenting Xu, Junyang Chen, Thomas Lukasiewicz

In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency.

Contrastive Learning Medical Report Generation

MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation

no code implementations27 Feb 2023 Xiangtao Wang, Ruizhi Wang, Biao Tian, Jiaojiao Zhang, Shuo Zhang, Junyang Chen, Thomas Lukasiewicz, Zhenghua Xu

We leverage the masked patches selection strategy to choose masked patches with lesions to obtain more lesion representation information, and the adaptive masking strategy is utilized to help learn more mutual information and improve performance further.

Contrastive Learning Image Segmentation +4

A Primary Frequency Control Strategy for Variable-Speed Pumped-Storage Plant in Power Generation Based on Adaptive Model Predictive Control

no code implementations1 Nov 2022 Zhenghua Xu, Changhong Deng, Qiuling Yang

Variable-speed pumped-storage (VSPS) has great potential in helping solve the frequency control problem caused by low inertia, owing to its remarkable flexibility beyond conventional fixed-speed one, to make better use of which, a primary frequency control strategy based on adaptive model predictive control (AMPC) is proposed in this paper for VSPS plant in power generation.

Model Predictive Control

Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty

no code implementations14 Oct 2022 Wenting Xu, Zhenghua Xu, Junyang Chen, Chang Qi, Thomas Lukasiewicz

In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems.

Medical Report Generation

PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training

1 code implementation24 Jul 2022 Zihang Xu, Zhenghua Xu, Shuo Zhang, Thomas Lukasiewicz

Unlike most existing semi-supervised learning methods, adversarial training based methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions.

Brain Tumor Segmentation Image Segmentation +2

Associative Memories via Predictive Coding

no code implementations NeurIPS 2021 Tommaso Salvatori, Yuhang Song, Yujian Hong, Simon Frieder, Lei Sha, Zhenghua Xu, Rafal Bogacz, Thomas Lukasiewicz

We conclude by discussing the possible impact of this work in the neuroscience community, by showing that our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.

Hippocampus Retrieval

RSG: A Simple but Effective Module for Learning Imbalanced Datasets

1 code implementation CVPR 2021 JianFeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Jianfei Cai, Zhenghua Xu

Imbalanced datasets widely exist in practice and area great challenge for training deep neural models with agood generalization on infrequent classes.

Long-tail Learning

Reverse Differentiation via Predictive Coding

no code implementations8 Mar 2021 Tommaso Salvatori, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz, Zhenghua Xu

Recent works prove that these methods can approximate BP up to a certain margin on multilayer perceptrons (MLPs), and asymptotically on any other complex model, and that zero-divergence inference learning (Z-IL), a variant of PC, is able to exactly implement BP on MLPs.

Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks

no code implementations NeurIPS 2020 Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz

However, there are several gaps between BP and learning in biologically plausible neuronal networks of the brain (learning in the brain, or simply BL, for short), in particular, (1) it has been unclear to date, if BP can be implemented exactly via BL, (2) there is a lack of local plasticity in BP, i. e., weight updates require information that is not locally available, while BL utilizes only locally available information, and (3)~there is a lack of autonomy in BP, i. e., some external control over the neural network is required (e. g., switching between prediction and learning stages requires changes to dynamics and synaptic plasticity rules), while BL works fully autonomously.

Reinforced Medical Report Generation with X-Linear Attention and Repetition Penalty

no code implementations16 Nov 2020 Wenting Xu, Chang Qi, Zhenghua Xu, Thomas Lukasiewicz

To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated with the classic encoder-decoder architecture to enhance the performance of deep models.

Decoder Medical Report Generation

SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images

no code implementations15 Nov 2020 Chang Qi, Junyang Chen, Guizhi Xu, Zhenghua Xu, Thomas Lukasiewicz, Yang Liu

We first generate MRI images on limited datasets, then we trained three popular classification models to get the best model for tumor classification.

Data Augmentation General Classification +2

Efficient Medical Image Segmentation with Intermediate Supervision Mechanism

no code implementations15 Nov 2020 Di Yuan, Junyang Chen, Zhenghua Xu, Thomas Lukasiewicz, Zhigang Fu, Guizhi Xu

However, U-Net is mainly engaged in segmentation, and the extracted features are also targeted at segmentation location information, and the input and output are different.

Decoder Image Segmentation +3

Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey

no code implementations2 Nov 2020 Dan Zhao, Guizhi Xu, Zhenghua Xu, Thomas Lukasiewicz, Minmin Xue, Zhigang Fu

Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years, which constitutes a series of medical tasks, such as detection of tumor markers, the outline of tumor leisures, subtypes and stages of tumors, prediction of therapeutic effect, and drug development.

Detecting Beneficial Feature Interactions for Recommender Systems

4 code implementations2 Aug 2020 Yixin Su, Rui Zhang, Sarah Erfani, Zhenghua Xu

To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy.

Graph Classification Graph Neural Network +1

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards

1 code implementation12 May 2019 Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Shangtong Zhang, Andrzej Wojcicki, Mai Xu

Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i. e., playing games without extrinsic rewards but evaluated with extrinsic rewards.

Segmentation is All You Need

no code implementations30 Apr 2019 Zehua Cheng, Yuxiang Wu, Zhenghua Xu, Thomas Lukasiewicz, Weiyang Wang

Region proposal mechanisms are essential for existing deep learning approaches to object detection in images.

Face Detection Head Detection +5

Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce

no code implementations6 Jan 2019 Zehua Cheng, Zhenghua Xu

In order to save more communication bandwidth and preserve the accuracy on ring structure, which break the restrict as the node increase, we propose a new algorithm to measure the importance of gradients on large-scale cluster implementing ring all-reduce based on the size of the ratio of parameter calculation gradient to parameter value.

Diversity-Driven Extensible Hierarchical Reinforcement Learning

1 code implementation10 Nov 2018 Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Mai Xu

However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive.

Diversity Hierarchical Reinforcement Learning +2

Cannot find the paper you are looking for? You can Submit a new open access paper.