Search Results for author: Guang Yang

Found 74 papers, 10 papers with code

Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction

no code implementations21 Jun 2022 Michael Tanzer, Pedro Ferreira, Andrew Scott, Zohya Khalique, Maria Dwornik, Dudley Pennell, Guang Yang, Daniel Rueckert, Sonia Nielles-Vallespin

Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which no other imaging modality allows.

Ensemble Learning

CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention

1 code implementation20 Jun 2022 Xiaodan Xing, Jiahao Huang, Yang Nan, Yinzhe Wu, Chengjia Wang, Zhifan Gao, Simon Walsh, Guang Yang

The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance.

Image Generation

Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis

no code implementations8 Apr 2022 Chenglong Wang, Yun Liu, Fen Wang, Chengxiu Zhang, Yida Wang, Mei Yuan, Guang Yang

However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them.

Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

no code implementations1 Apr 2022 Jiahao Huang, Yingying Fang, Yang Nan, Huanjun Wu, Yinzhe Wu, Zhifan Gao, Yang Li, Zidong Wang, Pietro Lio, Daniel Rueckert, Yonina C. Eldar, Guang Yang

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.

Explainable Models Lesion Detection +1

ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation

no code implementations21 Mar 2022 Wenbo Zhang, Guang Yang, He Huang, Weiji Yang, Xiaomei Xu, Yongkai Liu, Xiaobo Lai

Moreover, the serious voxel imbalance between the brain tumor and the background as well as the different sizes and locations of the brain tumor makes the segmentation of 3D images a challenging problem.

Brain Tumor Segmentation Tumor Segmentation

DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

1 code implementation17 Mar 2022 Guang Yang, Juan Cao, Qiang Sheng, Peng Qi, Xirong Li, Jintao Li

However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.

Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology

no code implementations11 Mar 2022 Yang Nan, Fengyi Li, Peng Tang, Guyue Zhang, Caihong Zeng, Guotong Xie, Zhihong Liu, Guang Yang

Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task.

Fine-Grained Image Classification whole slide images

HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis

no code implementations9 Mar 2022 Xiaodan Xing, Javier Del Ser, Yinzhe Wu, Yang Li, Jun Xia, Lei Xu, David Firmin, Peter Gatehouse, Guang Yang

A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.

Decision Making Left Ventricle Segmentation

Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis

no code implementations7 Mar 2022 Chengjia Wang, Guang Yang, Giorgos Papanastasiou

Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms.

Image Generation Image Registration

Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

no code implementations11 Feb 2022 Ming Li, Yingying Fang, Zeyu Tang, Chibudom Onuorah, Jun Xia, Javier Del Ser, Simon Walsh, Guang Yang

We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data.

Computed Tomography (CT) Decision Making

Fast MRI Reconstruction: How Powerful Transformers Are?

1 code implementation23 Jan 2022 Jiahao Huang, Yinzhe Wu, Huanjun Wu, Guang Yang

In particular, a generative adversarial network (GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI reconstruction.

MRI Reconstruction

Online Attentive Kernel-Based Temporal Difference Learning

no code implementations22 Jan 2022 Guang Yang, Xingguo Chen, Shangdong Yang, Huihui Wang, Shaokang Dong, Yang Gao

Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA.

Acrobot

Swin Transformer for Fast MRI

2 code implementations10 Jan 2022 Jiahao Huang, Yingying Fang, Yinzhe Wu, Huanjun Wu, Zhifan Gao, Yang Li, Javier Del Ser, Jun Xia, Guang Yang

The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.

MRI Reconstruction

AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis

no code implementations23 Dec 2021 Yutong Chen, Carola-Bibiane Schönlieb, Pietro Liò, Tim Leiner, Pier Luigi Dragotti, Ge Wang, Daniel Rueckert, David Firmin, Guang Yang

Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images

no code implementations9 Dec 2021 Qinghao Ye, Yuan Gao, Weiping Ding, Zhangming Niu, Chengjia Wang, Yinghui Jiang, Minhao Wang, Evandro Fei Fang, Wade Menpes-Smith, Jun Xia, Guang Yang

The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.

Computed Tomography (CT)

Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation

no code implementations31 Oct 2021 Michael Yeung, Guang Yang, Evis Sala, Carola-Bibiane Schönlieb, Leonardo Rundo

Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.

Semantic Segmentation

Focal Attention Networks: optimising attention for biomedical image segmentation

no code implementations31 Oct 2021 Michael Yeung, Leonardo Rundo, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang

In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.

Semantic Segmentation

Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation

1 code implementation31 Oct 2021 Michael Yeung, Leonardo Rundo, Yang Nan, Evis Sala, Carola-Bibiane Schönlieb, Guang Yang

However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians.

Semantic Segmentation

Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data

1 code implementation17 Sep 2021 Jun Chen, Heye Zhang, Raad Mohiaddin, Tom Wong, David Firmin, Jennifer Keegan, Guang Yang

For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains.

Left Atrium Segmentation

FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution

no code implementations9 Aug 2021 Mingfeng Jiang, Minghao Zhi, Liying Wei, Xiaocheng Yang, Jucheng Zhang, Yongming Li, Pin Wang, Jiahao Huang, Guang Yang

High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time.

Image Super-Resolution SSIM

3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework

no code implementations26 Jul 2021 Xi Guan, Guang Yang, Jianming Ye, Weiji Yang, Xiaomei Xu, Weiwei Jiang, Xiaobo Lai

Background: Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health.

Brain Tumor Segmentation MRI segmentation +1

High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss

no code implementations21 Jul 2021 Guangyuan Li, Jun Lv, Xiangrong Tong, Chengyan Wang, Guang Yang

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations.

MRI Reconstruction Super-Resolution

RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data

no code implementations19 Jul 2021 Guang Yang, Ke Mu, Chunhe Song, Zhijia Yang, Tierui Gong

Federated learning is a widely used distributed deep learning framework that protects the privacy of each client by exchanging model parameters rather than raw data.

Federated Learning

Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives

no code implementations28 Jun 2021 Yinzhe Wu, Zeyu Tang, Binghuan Li, David Firmin, Guang Yang

Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably.

MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution

no code implementations22 May 2021 Jin Zhu, Chuan Tan, Junwei Yang, Guang Yang, Pietro Lio'

We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT).

Image Super-Resolution Meta-Learning +2

Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction

1 code implementation17 May 2021 Jun Lv, Guangyuan Li, Xiangrong Tong, Weibo Chen, Jiahao Huang, Chengyan Wang, Guang Yang

The transfer learning results for the knee and liver were superior to those of the PI-GAN model trained with its own dataset using a smaller number of training cases.

MRI Reconstruction Transfer Learning

Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives

no code implementations4 May 2021 Guang Yang, Jun Lv, Yutong Chen, Jiahao Huang, Jin Zhu

However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical.

Compressive Sensing MRI Reconstruction

JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

no code implementations1 May 2021 Jun Chen, Guang Yang, Habib Khan, Heye Zhang, Yanping Zhang, Shu Zhao, Raad Mohiaddin, Tom Wong, David Firmin, Jennifer Keegan

In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way.

Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study

no code implementations25 Apr 2021 Qinghao Ye, Jun Xia, Guang Yang

XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand.

Decision Making

Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond

no code implementations3 Feb 2021 Guang Yang, Qinghao Ye, Jun Xia

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made.

Decision Making Explainable artificial intelligence

Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion

no code implementations ICCV 2021 Qinghao Ye, Xiyue Shen, Yuan Gao, ZiRui Wang, Qi Bi, Ping Li, Guang Yang

Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i. e., where the highlight moments are in long videos) for supervised learning.

Highlight Detection

Automated Multi-Channel Segmentation for the 4D Myocardial Velocity Mapping Cardiac MR

no code implementations16 Dec 2020 Yinzhe Wu, Suzan Hatipoglu, Diego Alonso-Álvarez, Peter Gatehouse, David Firmin, Jennifer Keegan, Guang Yang

Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.

Annealing Genetic GAN for Minority Oversampling

no code implementations5 Aug 2020 Jingyu Hao, Chengjia Wang, Heye Zhang, Guang Yang

In particular, the generator uses different training strategies to generate multiple offspring and retain the best.

Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness

no code implementations23 Jun 2020 Yifeng Guo, Chengjia Wang, Heye Zhang, Guang Yang

The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts.

Compressive Sensing MRI Reconstruction

Gleason Score Prediction using Deep Learning in Tissue Microarray Image

no code implementations11 May 2020 Yi-Hong Zhang, Jing Zhang, Yang song, Chaomin Shen, Guang Yang

Prostate cancer (PCa) is one of the most common cancers in men around the world.

MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis

no code implementations1 May 2020 Ming Li, Chengjia Wang, Heye Zhang, Guang Yang

In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle.

Decision Making

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images

no code implementations14 Apr 2020 Shaoping Hu, Yuan Gao, Zhangming Niu, Yinghui Jiang, Lao Li, Xianglu Xiao, Minhao Wang, Evandro Fei Fang, Wade Menpes-Smith, Jun Xia, Hui Ye, Guang Yang

An outbreak of a novel coronavirus disease (i. e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world.

General Classification Respiratory Failure

Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention

no code implementations2 Feb 2020 Guang Yang, Jun Chen, Zhifan Gao, Shuo Li, Hao Ni, Elsa Angelini, Tom Wong, Raad Mohiaddin, Eva Nyktari, Ricardo Wage, Lei Xu, Yanping Zhang, Xiuquan Du, Heye Zhang, David Firmin, Jennifer Keegan

Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).

Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning

no code implementations9 Jan 2020 Sen Lin, Guang Yang, Junshan Zhang

Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge.

Meta-Learning

Distributed and Consistent Multi-Image Feature Matching via QuickMatch

no code implementations29 Oct 2019 Zachary Serlin, Guang Yang, Brandon Sookraj, Calin Belta, Roberto Tron

The centralized QuickMatch algorithm is compared to other standard matching algorithms, while the Distributed QuickMatch algorithm is compared to the centralized algorithm in terms of preservation of match consistency.

object-detection Object Detection +1

MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks

no code implementations13 Sep 2019 Mohammadreza Soltaninejad, Lei Zhang, Tryphon Lambrou, Guang Yang, Nigel Allinson, Xujiong Ye

In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features.

Brain Tumor Segmentation Tumor Segmentation

Discriminative Consistent Domain Generation for Semi-supervised Learning

no code implementations24 Jul 2019 Jun Chen, Heye Zhang, Yanping Zhang, Shu Zhao, Raad Mohiaddin, Tom Wong, David Firmin, Guang Yang, Jennifer Keegan

Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation.

Domain Adaptation Image Generation

Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

no code implementations20 Jul 2019 Dong Zhang, Guang Yang, Shu Zhao, Yanping Zhang, Heye Zhang, Shuo Li

The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation.

Multiview Learning

Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework

no code implementations21 Feb 2019 Lei Li, Fuping Wu, Guang Yang, Lingchao Xu, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Xiahai Zhuang

Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0. 01).

Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines

no code implementations20 Feb 2019 Cun Mu, Jun Zhao, Guang Yang, Binwei Yang, Zheng Yan

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines.

Information Retrieval Representation Learning

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

Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images

no code implementations22 Oct 2018 Fuping Wu, Lei LI, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Lingchao Xu, Xiahai Zhuang

We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session.

Texture Classification

Atrial scars segmentation via potential learning in the graph-cuts framework

no code implementations22 Oct 2018 Lei Li, Fuping Wu, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jenny Keegan, Lingchao Xu, Xiahai Zhuang

Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF).

Lesion Focused Super-Resolution

no code implementations15 Oct 2018 Jin Zhu, Guang Yang, Pietro Lio

Super-resolution (SR) for image enhancement has great importance in medical image applications.

Brain Tumor Segmentation Image Enhancement +3

A Machine Learning Approach to Shipping Box Design

no code implementations26 Sep 2018 Guang Yang, Cun Mu

Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers.

Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models

no code implementations23 Aug 2018 Nathan J Olliverre, Guang Yang, Gregory Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

1 code implementation28 Jun 2018 Maximilian Seitzer, Guang Yang, Jo Schlemper, Ozan Oktay, Tobias Würfl, Vincent Christlein, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Daniel Rueckert, Andreas Maier

In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.

MRI Reconstruction

Towards Practical Visual Search Engine within Elasticsearch

no code implementations23 Jun 2018 Cun Mu, Jun Zhao, Guang Yang, Jing Zhang, Zheng Yan

In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine.

Content-Based Image Retrieval

Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation

no code implementations12 Jun 2018 Jun Chen, Guang Yang, Zhifan Gao, Hao Ni, Elsa Angelini, Raad Mohiaddin, Tom Wong, Yanping Zhang, Xiuquan Du, Heye Zhang, Jennifer Keegan, David Firmin

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.

A two-stage 3D Unet framework for multi-class segmentation on full resolution image

no code implementations12 Apr 2018 Chengjia Wang, Tom MacGillivray, Gillian Macnaught, Guang Yang, David Newby

Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances.

Image Super-Resolution

Revisiting Skip-Gram Negative Sampling Model with Rectification

no code implementations1 Apr 2018 Cun Mu, Guang Yang, Zheng Yan

We revisit skip-gram negative sampling (SGNS), one of the most popular neural-network based approaches to learning distributed word representation.

Deep De-Aliasing for Fast Compressive Sensing MRI

no code implementations19 May 2017 Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.

Compressive Sensing De-aliasing +1

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

no code implementations10 May 2017 Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo

In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent.

Brain Tumor Segmentation Tumor Segmentation

The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation

no code implementations27 Mar 2017 Yuanhan Mo, Fangde Liu, Douglas McIlwraith, Guang Yang, Jingqing Zhang, Taigang He, Yike Guo

Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge.

Left Ventricle Segmentation

Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization

no code implementations7 Nov 2016 Sean C. Smithson, Guang Yang, Warren J. Gross, Brett H. Meyer

The method is evaluated on the MNIST and CIFAR-10 image datasets, optimizing for both recognition accuracy and computational complexity.

Image Classification speech-recognition +1

Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis

1 code implementation23 Jul 2013 Guang Yang, John H. Hipwell, David J. Hawkes, Simon R. Arridge

We evaluate our methods using various computational digital phantoms, uncompressed breast MR images, and in-vivo DBT simulations.

Image Registration

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