Search Results for author: Jianpeng Zhang

Found 28 papers, 12 papers with code

Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning

1 code implementation29 Nov 2023 Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Qi Wu, Yong Xia

In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data.

Continual Learning Representation Learning +1

Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists

no code implementations20 Jul 2023 Jianpeng Zhang, Xianghua Ye, Jianfeng Zhang, Yuxing Tang, Minfeng Xu, Jianfei Guo, Xin Chen, Zaiyi Liu, Jingren Zhou, Le Lu, Ling Zhang

In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.

Decision Making

Attention Mechanisms in Medical Image Segmentation: A Survey

no code implementations29 May 2023 Yutong Xie, Bing Yang, Qingbiao Guan, Jianpeng Zhang, Qi Wu, Yong Xia

This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation.

Image Segmentation Medical Image Segmentation +3

UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner

1 code implementation7 Apr 2023 Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia

Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation.

Image Segmentation Medical Image Segmentation +2

Biomedical image analysis competitions: The state of current participation practice

no code implementations16 Dec 2022 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, Alison Noble, Carlos Ortiz de Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne Martel, Carlos Martín-Isla, Bjoern Menze, Chinedu Innocent Nwoye, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Carole Sudre, Kimberlin Van Wijnen, Armine Vardazaryan, Tom Vercauteren, Martin Wagner, Chuanbo Wang, Moi Hoon Yap, Zeyun Yu, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Rina Bao, Chanyeol Choi, Andrew Cohen, Oleh Dzyubachyk, Adrian Galdran, Tianyuan Gan, Tianqi Guo, Pradyumna Gupta, Mahmood Haithami, Edward Ho, Ikbeom Jang, Zhili Li, Zhengbo Luo, Filip Lux, Sokratis Makrogiannis, Dominik Müller, Young-tack Oh, Subeen Pang, Constantin Pape, Gorkem Polat, Charlotte Rosalie Reed, Kanghyun Ryu, Tim Scherr, Vajira Thambawita, Haoyu Wang, Xinliang Wang, Kele Xu, Hung Yeh, Doyeob Yeo, Yixuan Yuan, Yan Zeng, Xin Zhao, Julian Abbing, Jannes Adam, Nagesh Adluru, Niklas Agethen, Salman Ahmed, Yasmina Al Khalil, Mireia Alenyà, Esa Alhoniemi, Chengyang An, Talha Anwar, Tewodros Weldebirhan Arega, Netanell Avisdris, Dogu Baran Aydogan, Yingbin Bai, Maria Baldeon Calisto, Berke Doga Basaran, Marcel Beetz, Cheng Bian, Hao Bian, Kevin Blansit, Louise Bloch, Robert Bohnsack, Sara Bosticardo, Jack Breen, Mikael Brudfors, Raphael Brüngel, Mariano Cabezas, Alberto Cacciola, Zhiwei Chen, Yucong Chen, Daniel Tianming Chen, Minjeong Cho, Min-Kook Choi, Chuantao Xie Chuantao Xie, Dana Cobzas, Julien Cohen-Adad, Jorge Corral Acero, Sujit Kumar Das, Marcela de Oliveira, Hanqiu Deng, Guiming Dong, Lars Doorenbos, Cory Efird, Sergio Escalera, Di Fan, Mehdi Fatan Serj, Alexandre Fenneteau, Lucas Fidon, Patryk Filipiak, René Finzel, Nuno R. Freitas, Christoph M. Friedrich, Mitchell Fulton, Finn Gaida, Francesco Galati, Christoforos Galazis, Chang Hee Gan, Zheyao Gao, Shengbo Gao, Matej Gazda, Beerend Gerats, Neil Getty, Adam Gibicar, Ryan Gifford, Sajan Gohil, Maria Grammatikopoulou, Daniel Grzech, Orhun Güley, Timo Günnemann, Chunxu Guo, Sylvain Guy, Heonjin Ha, Luyi Han, Il Song Han, Ali Hatamizadeh, Tian He, Jimin Heo, Sebastian Hitziger, SeulGi Hong, Seungbum Hong, Rian Huang, Ziyan Huang, Markus Huellebrand, Stephan Huschauer, Mustaffa Hussain, Tomoo Inubushi, Ece Isik Polat, Mojtaba Jafaritadi, SeongHun Jeong, Bailiang Jian, Yuanhong Jiang, Zhifan Jiang, Yueming Jin, Smriti Joshi, Abdolrahim Kadkhodamohammadi, Reda Abdellah Kamraoui, Inha Kang, Junghwa Kang, Davood Karimi, April Khademi, Muhammad Irfan Khan, Suleiman A. Khan, Rishab Khantwal, Kwang-Ju Kim, Timothy Kline, Satoshi Kondo, Elina Kontio, Adrian Krenzer, Artem Kroviakov, Hugo Kuijf, Satyadwyoom Kumar, Francesco La Rosa, Abhi Lad, Doohee Lee, Minho Lee, Chiara Lena, Hao Li, Ling Li, Xingyu Li, Fuyuan Liao, Kuanlun Liao, Arlindo Limede Oliveira, Chaonan Lin, Shan Lin, Akis Linardos, Marius George Linguraru, Han Liu, Tao Liu, Di Liu, Yanling Liu, João Lourenço-Silva, Jingpei Lu, Jiangshan Lu, Imanol Luengo, Christina B. Lund, Huan Minh Luu, Yi Lv, Uzay Macar, Leon Maechler, Sina Mansour L., Kenji Marshall, Moona Mazher, Richard McKinley, Alfonso Medela, Felix Meissen, Mingyuan Meng, Dylan Miller, Seyed Hossein Mirjahanmardi, Arnab Mishra, Samir Mitha, Hassan Mohy-ud-Din, Tony Chi Wing Mok, Gowtham Krishnan Murugesan, Enamundram Naga Karthik, Sahil Nalawade, Jakub Nalepa, Mohamed Naser, Ramin Nateghi, Hammad Naveed, Quang-Minh Nguyen, Cuong Nguyen Quoc, Brennan Nichyporuk, Bruno Oliveira, David Owen, Jimut Bahan Pal, Junwen Pan, Wentao Pan, Winnie Pang, Bogyu Park, Vivek Pawar, Kamlesh Pawar, Michael Peven, Lena Philipp, Tomasz Pieciak, Szymon Plotka, Marcel Plutat, Fattaneh Pourakpour, Domen Preložnik, Kumaradevan Punithakumar, Abdul Qayyum, Sandro Queirós, Arman Rahmim, Salar Razavi, Jintao Ren, Mina Rezaei, Jonathan Adam Rico, ZunHyan Rieu, Markus Rink, Johannes Roth, Yusely Ruiz-Gonzalez, Numan Saeed, Anindo Saha, Mostafa Salem, Ricardo Sanchez-Matilla, Kurt Schilling, Wei Shao, Zhiqiang Shen, Ruize Shi, Pengcheng Shi, Daniel Sobotka, Théodore Soulier, Bella Specktor Fadida, Danail Stoyanov, Timothy Sum Hon Mun, Xiaowu Sun, Rong Tao, Franz Thaler, Antoine Théberge, Felix Thielke, Helena Torres, Kareem A. Wahid, Jiacheng Wang, Yifei Wang, Wei Wang, Xiong Wang, Jianhui Wen, Ning Wen, Marek Wodzinski, Ye Wu, Fangfang Xia, Tianqi Xiang, Chen Xiaofei, Lizhan Xu, Tingting Xue, Yuxuan Yang, Lin Yang, Kai Yao, Huifeng Yao, Amirsaeed Yazdani, Michael Yip, Hwanseung Yoo, Fereshteh Yousefirizi, Shunkai Yu, Lei Yu, Jonathan Zamora, Ramy Ashraf Zeineldin, Dewen Zeng, Jianpeng Zhang, Bokai Zhang, Jiapeng Zhang, Fan Zhang, Huahong Zhang, Zhongchen Zhao, Zixuan Zhao, Jiachen Zhao, Can Zhao, Qingshuo Zheng, Yuheng Zhi, Ziqi Zhou, Baosheng Zou, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

Of these, 84% were based on standard architectures.

Benchmarking

Learning from partially labeled data for multi-organ and tumor segmentation

1 code implementation13 Nov 2022 Yutong Xie, Jianpeng Zhang, Yong Xia, Chunhua Shen

To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets.

Image Segmentation Medical Image Segmentation +4

ClusTR: Exploring Efficient Self-attention via Clustering for Vision Transformers

no code implementations28 Aug 2022 Yutong Xie, Jianpeng Zhang, Yong Xia, Anton Van Den Hengel, Qi Wu

Besides, we further extend the clustering-guided attention from single-scale to multi-scale, which is conducive to dense prediction tasks.

Clustering Language Modelling

UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier

1 code implementation17 Dec 2021 Yutong Xie, Jianpeng Zhang, Yong Xia, Qi Wu

In this paper, we advocate bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS.

Image Classification Medical Image Classification +2

Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation

1 code implementation13 Sep 2021 Shishuai Hu, Zehui Liao, Jianpeng Zhang, Yong Xia

In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain.

Domain Generalization Image Segmentation +4

Kernel Adversarial Learning for Real-world Image Super-resolution

no code implementations19 Apr 2021 Hu Wang, Congbo Ma, Jianpeng Zhang, Gustavo Carneiro

Current deep image super-resolution (SR) approaches attempt to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises.

Image Super-Resolution

CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation

1 code implementation4 Mar 2021 Yutong Xie, Jianpeng Zhang, Chunhua Shen, Yong Xia

Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.

Image Segmentation Inductive Bias +4

Inter-slice Context Residual Learning for 3D Medical Image Segmentation

1 code implementation28 Nov 2020 Jianpeng Zhang, Yutong Xie, Yan Wang, Yong Xia

In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images.

Brain Tumor Segmentation Image Segmentation +3

PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation

no code implementations25 Nov 2020 Yutong Xie, Jianpeng Zhang, Zehui Liao, Yong Xia, Chunhua Shen

In this paper, we propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space.

Image Segmentation Medical Image Segmentation +3

DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets

1 code implementation CVPR 2021 Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen

To address this, we propose a dynamic on-demand network (DoDNet) that learns to segment multiple organs and tumors on partially labeled datasets.

Image Segmentation Medical Image Segmentation +4

Pairwise Relation Learning for Semi-supervised Gland Segmentation

no code implementations6 Aug 2020 Yutong Xie, Jianpeng Zhang, Zhibin Liao, Chunhua Shen, Johan Verjans, Yong Xia

In this paper, we propose the pairwise relation-based semi-supervised (PRS^2) model for gland segmentation on histology images.

Relation Relation Network +1

Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection

1 code implementation27 Mar 2020 Jianpeng Zhang, Yutong Xie, Guansong Pang, Zhibin Liao, Johan Verjans, Wenxin Li, Zongji Sun, Jian He, Yi Li, Chunhua Shen, Yong Xia

In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into an one-class classification-based anomaly detection problem, and thus propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module.

Binary Classification Classification +2

A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification

no code implementations17 Aug 2019 Yang Liu, Jianpeng Zhang, Chao GAO, Jinghua Qu, Lixin Ji

In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e. g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function.

General Classification Sentence +1

Natural-Logarithm-Rectified Activation Function in Convolutional Neural Networks

no code implementations10 Aug 2019 Yang Liu, Jianpeng Zhang, Chao GAO, Jinghua Qu, Lixin Ji

Activation functions play a key role in providing remarkable performance in deep neural networks, and the rectified linear unit (ReLU) is one of the most widely used activation functions.

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

1 code implementation8 Mar 2019 Yutong Xie, Jianpeng Zhang, Yong Xia, Chunhua Shen

Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

Classification General Classification +3

A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images

no code implementations23 Jul 2018 Yutong Xie, Jianpeng Zhang, Yong Xia

A multi-level deep ensemble (MLDE) model that can be trained in an 'end to end' manner is proposed for skin lesion classification in dermoscopy images.

General Classification Lesion Classification +1

struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding

no code implementations25 May 2018 Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy

Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.

Clustering Network Embedding

Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning

no code implementations28 Jun 2017 Jianpeng Zhang, Yong Xia, Qi Wu, Yutong Xie

The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes.

General Classification Image Classification +3

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