Search Results for author: Yong Xia

Found 74 papers, 37 papers with code

PairAug: What Can Augmented Image-Text Pairs Do for Radiology?

2 code implementations7 Apr 2024 Yutong Xie, Qi Chen, Sinuo Wang, Minh-Son To, Iris Lee, Ee Win Khoo, Kerolos Hendy, Daniel Koh, Yong Xia, Qi Wu

Acknowledging this limitation, our objective is to devise a framework capable of concurrently augmenting medical image and text data.

Image Classification Language Modelling +3

Learning with Diversification from Block Sparse Signal

no code implementations7 Feb 2024 Yanhao Zhang, Zhihan Zhu, Yong Xia

This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data.

Sparse Learning

SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation

2 code implementations22 Dec 2023 Wenxi Yue, Jing Zhang, Kun Hu, Qiuxia Wu, ZongYuan Ge, Yong Xia, Jiebo Luo, Zhiyong Wang

Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts; (2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations; and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios.

Segmentation Semantic Segmentation

Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts

no code implementations5 Dec 2023 Jiayi Chen, Benteng Ma, Hengfei Cui, Yong Xia, Kwang-Ting Cheng

To mitigate this issue, federated active learning methods suggest leveraging local and global model predictions to select a relatively small amount of informative local data for annotation.

Active Learning Federated Learning +1

Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation

1 code implementation30 Nov 2023 Ziyang Chen, Yiwen Ye, Mengkang Lu, Yongsheng Pan, Yong Xia

Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model in real-world applications.

Image Segmentation Medical Image Segmentation +2

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

UAE: Universal Anatomical Embedding on Multi-modality Medical Images

1 code implementation25 Nov 2023 Xiaoyu Bai, Fan Bai, Xiaofei Huo, Jia Ge, JingJing Lu, Xianghua Ye, Ke Yan, Yong Xia

They use self-supervised learning to acquire a discriminative embedding for each voxel within the image.

Self-Supervised Learning

Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

no code implementations20 Nov 2023 Qingjie Zeng, Yutong Xie, Zilin Lu, Mengkang Lu, Yicheng Wu, Yong Xia

Therefore, in this paper, we introduce a \textbf{Ver}satile \textbf{Semi}-supervised framework (VerSemi) to point out a new perspective that integrates various tasks into a unified model with a broad label space, to exploit more unlabeled data for semi-supervised medical image segmentation.

Benchmarking Image Segmentation +3

Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation

1 code implementation26 Sep 2023 Qingjie Zeng, Yutong Xie, Zilin Lu, Mengkang Lu, Yong Xia

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data especially on the task of volumetric medical image segmentation.

Image Segmentation Semantic Segmentation +2

Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training

no code implementations23 Sep 2023 Xiaoyu Bai, Benteng Ma, Changyang Li, Yong Xia

Pseudo-label-based methods examine the training data and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue.

Lesion Detection Pseudo Label

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

1 code implementation17 Aug 2023 Wenxi Yue, Jing Zhang, Kun Hu, Yong Xia, Jiebo Luo, Zhiyong Wang

However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to inferior generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline.

Image Segmentation Segmentation +1

URL: Combating Label Noise for Lung Nodule Malignancy Grading

2 code implementations17 Aug 2023 Xianze Ai, Zehui Liao, Yong Xia

Although researchers adopt the label-noise-robust methods to handle label noise for lung nodule malignancy grading, they do not consider the inherent ordinal relation among classes of this task.

Contrastive Learning Relation

SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference

no code implementations24 Jun 2023 Xiaoyu Bai, Yong Xia

Medical images like CT and MRI provide detailed information about the internal structure of the body, and identifying key anatomical structures from these images plays a crucial role in clinical workflows.

Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation

1 code implementation8 Jun 2023 Shishuai Hu, Zehui Liao, Yong Xia

In C$^2$SDG, the shallower features of each image and its style-augmented counterpart are extracted and used for contrastive training, resulting in the disentangled style representations and structure representations.

Disentanglement Domain Generalization +6

TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation

1 code implementation7 Jun 2023 Rui Sun, Tao Lei, Weichuan Zhang, Yong Wan, Yong Xia, Asoke K. Nandi

The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation.

Image Segmentation Medical Image Segmentation +2

Transformer-based Annotation Bias-aware Medical Image Segmentation

no code implementations2 Jun 2023 Zehui Liao, Yutong Xie, Shishuai Hu, Yong Xia

This paper proposes a Transformer-based Annotation Bias-aware (TAB) medical image segmentation model, which tackles the annotator-related bias via modeling annotator preference and stochastic errors.

Image Segmentation Medical Image Segmentation +2

Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation

1 code implementation31 May 2023 Ziyang Chen, Yongsheng Pan, Yiwen Ye, Hengfei Cui, Yong Xia

In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution.

Domain Generalization Image Segmentation +2

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

Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation

no code implementations10 Apr 2023 Ziyang Chen, Yongsheng Pan, Yong Xia

The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way.

Edge Detection Segmentation +1

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

An End-to-End Framework For Universal Lesion Detection With Missing Annotations

no code implementations27 Mar 2023 Xiaoyu Bai, Yong Xia

In this work, we present a novel end-to-end framework for mining unlabeled lesions while simultaneously training the detector.

Lesion Detection

DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions

1 code implementation Expert Systems with Applications 2023 Ibtihaj Ahmad, Yong Xia, Hengfei Cui, Zain Ul Islam

The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tissue variability, and conditions having higher morphological variability.

Instance Segmentation Nuclei Classification +2

PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training

no code implementations CVPR 2023 Qingjie Zeng, Yutong Xie, Zilin Lu, Yong Xia

In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multi-class and multi-label medical image classification from the point of loss distribution modeling and adversarial training.

Image Classification Semi-supervised Medical Image Classification

Instance-specific Label Distribution Regularization for Learning with Label Noise

no code implementations16 Dec 2022 Zehui Liao, Shishuai Hu, Yutong Xie, Yong Xia

Specifically, we estimate the noisy posterior under the supervision of noisy labels, and approximate the batch-level noise transition matrix by estimating the inter-class correlation matrix with neither anchor points nor pseudo anchor points.

ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation

1 code implementation21 Nov 2022 Shishuai Hu, Zehui Liao, Yong Xia

In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy.

Image Segmentation Medical Image Segmentation +5

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

Boundary-Aware Network for Abdominal Multi-Organ Segmentation

1 code implementation29 Aug 2022 Shishuai Hu, Zehui Liao, Yong Xia

In this paper, we propose a boundary-aware network (BA-Net) to segment abdominal organs on CT scans and MRI scans.

Image Segmentation Medical Image Segmentation +3

Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

1 code implementation29 Aug 2022 Shishuai Hu, Zehui Liao, Yong Xia

Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis.

Image Segmentation Medical Image Segmentation +2

Boundary-Aware Network for Kidney Parsing

1 code implementation29 Aug 2022 Shishuai Hu, Yiwen Ye, Zehui Liao, Yong Xia

Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings.

Image Segmentation Medical Image Segmentation +2

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

HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging

no code implementations10 Feb 2022 Haozhe Jia, Chao Bai, Weidong Cai, Heng Huang, Yong Xia

In our previous work, $i. e.$, HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging.

Brain Tumor Segmentation Segmentation +1

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

Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint

no code implementations4 Dec 2021 Xinke Ma, Yibo Yang, Yong Xia, DaCheng Tao

In this paper, we present a novel dual-flow transformation network with region consistency constraint which maximizes the similarity of ROIs within a pair of images and estimates both global and region spatial transformations simultaneously.

Image Registration

FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis

3 code implementations CVPR 2022 Yu Feng, Benteng Ma, Jing Zhang, Shanshan Zhao, Yong Xia, DaCheng Tao

However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).

Artifact Detection Backdoor Attack +6

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

2 code implementations21 Sep 2021 Yicheng Wu, ZongYuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia, Jianfei Cai

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.

Image Segmentation Segmentation +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

Boundary-aware Graph Reasoning for Semantic Segmentation

no code implementations9 Aug 2021 Haoteng Tang, Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia, Liang Zhan

In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.

graph construction Segmentation +1

PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images

no code implementations9 Aug 2021 Haozhe Jia, Haoteng Tang, Guixiang Ma, Weidong Cai, Heng Huang, Liang Zhan, Yong Xia

In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel.

Computed Tomography (CT) graph construction +3

Learning Synergistic Attention for Light Field Salient Object Detection

1 code implementation28 Apr 2021 Yi Zhang, Geng Chen, Qian Chen, Yujia Sun, Yong Xia, Olivier Deforges, Wassim Hamidouche, Lu Zhang

We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.

Object object-detection +2

Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction

1 code implementation23 Apr 2021 Zehui Liao, Yutong Xie, Shishuai Hu, Yong Xia

According to the consistency and reliability of their annotations, we divide nodules into three sets: a consistent and reliable set (CR-Set), an inconsistent set (IC-Set), and a low reliable set (LR-Set).


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

Deciding whether two quadratic surfaces actually intersect

no code implementations18 Dec 2020 Huu-Quang Nguyen, Ruey-Lin Sheu, Yong Xia

We answer an open question proposed by P\'{o}lik and Terlaky in 2007 that: {\it how we can decide whether two quadratic surfaces intersect without actually computing the intersections?}

Optimization and Control 90C20, 90C22, 90C26 F.2

Solving a new type of quadratic optimization problem having a joint numerical range constraint

no code implementations18 Dec 2020 Huu-Quang Nguyen, Ruey-Lin Sheu, Yong Xia

The objective function $F(f(x), g(x))$ is given as composition of a quadratic function $F(z)$ with two $n$-variate quadratic functions $z_1=f(x)$ and $z_2=g(x).$ In addition, it incorporates with a set of linear inequality constraints in $z=(z_1, z_2)^T,$ while having an implicit constraint that $z$ belongs to the joint numerical range of $(f, g).$ The formulation is very general in the sense that it covers quadratic programming with a single quadratic constraint of all types, including the inequality-type, the equality-type, and the interval-type.

Optimization and Control 90C20, 90C22, 90C26 F.2

Auto Learning Attention

1 code implementation NeurIPS 2020 Benteng Ma, Jing Zhang, Yong Xia, DaCheng Tao

Attention modules have been demonstrated effective in strengthening the representation ability of a neural network via reweighting spatial or channel features or stacking both operations sequentially.

Image Classification Keypoint Detection +2

Inter-layer Transition in Neural Architecture Search

1 code implementation30 Nov 2020 Benteng Ma, Jing Zhang, Yong Xia, DaCheng Tao

Differential Neural Architecture Search (NAS) methods represent the network architecture as a repetitive proxy directed acyclic graph (DAG) and optimize the network weights and architecture weights alternatively in a differential manner.

Neural Architecture Search

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

Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation

no code implementations9 Oct 2020 Yicheng Wu, Chengwei Pan, Shuqi Wang, Ming Zhang, Yong Xia, Yizhou Yu

Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases.

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 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

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Residual Network based Aggregation Model for Skin Lesion Classification

no code implementations24 Jul 2018 Yongsheng Pan, Yong Xia

We recognize that the skin lesion diagnosis is an essential and challenging sub-task in Image classification, in which the Fisher vector (FV) encoding algorithm and deep convolutional neural network (DCNN) are two of the most successful techniques.

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

3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation

no code implementations18 Jul 2018 Haozhe Jia, Yang song, Donghao Zhang, Heng Huang, Dagan Feng, Michael Fulham, Yong Xia, Weidong Cai

In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.

General Classification Segmentation

ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography

no code implementations9 Jul 2018 Hongyu Wang, Yong Xia

Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography.

General Classification Weakly-supervised Learning

A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency

no code implementations2 Jul 2018 Jun-Jie Zhang, Yong Xia, Yanning Zhang

Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care.

Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification

no code implementations1 Jul 2018 Benteng Ma, Yong Xia

Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications.

Classification General Classification +1

Detecting atrial fibrillation by deep convolutional neural networks

no code implementations18 Feb 2018 Yong Xia, Naren Wulan, Kuanquan Wang, Henggui Zhang

Conclusion The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection.


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 +2

A Tribe Competition-Based Genetic Algorithm for Feature Selection in Pattern Classification

no code implementations28 Apr 2017 Benteng Ma, Yong Xia

In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification.

Evolutionary Algorithms feature selection +1

Similar Handwritten Chinese Character Discrimination by Weakly Supervised Learning

no code implementations19 Sep 2015 Zhibo Yang, Huanle Xu, Keda Fu, Yong Xia

The unconstrained property makes our method well adapted to high variance in the size and position of discriminative regions in similar handwritten Chinese characters.

Weakly-supervised Learning

Robust Saliency Detection via Regularized Random Walks Ranking

no code implementations CVPR 2015 Changyang Li, Yuchen Yuan, Weidong Cai, Yong Xia, David Dagan Feng

In the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image.

Saliency Detection

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