Search Results for author: Yanwu Xu

Found 68 papers, 32 papers with code

Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training

no code implementations16 Jan 2024 Jiamin Chen, Xuhong LI, Yanwu Xu, Mengnan Du, Haoyi Xiong

Based on a large-scale medical image classification dataset, our work collects explanations from well-trained classifiers to generate pseudo labels of segmentation tasks.

Image Classification Image Segmentation +4

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models

no code implementations5 Dec 2023 Shaoan Xie, Yang Zhao, Zhisheng Xiao, Kelvin C. K. Chan, Yandong Li, Yanwu Xu, Kun Zhang, Tingbo Hou

Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting.

Image Inpainting

Towards General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

2 code implementations4 Dec 2023 Mohammed Baharoon, Waseem Qureshi, Jiahong Ouyang, Yanwu Xu, Abdulrhman Aljouie, Wei Peng

To measure the effectiveness and generalizability of DINOv2's feature representations, we analyze the model across medical image analysis tasks including disease classification and organ segmentation on both 2D and 3D images, and under different settings like kNN, few-shot learning, linear-probing, end-to-end fine-tuning, and parameter-efficient fine-tuning.

Few-Shot Learning Organ Segmentation +1

MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices

no code implementations28 Nov 2023 Yang Zhao, Yanwu Xu, Zhisheng Xiao, Tingbo Hou

The deployment of large-scale text-to-image diffusion models on mobile devices is impeded by their substantial model size and slow inference speed.

Computational Efficiency Text-to-Image Generation

UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs

no code implementations14 Nov 2023 Yanwu Xu, Yang Zhao, Zhisheng Xiao, Tingbo Hou

Text-to-image diffusion models have demonstrated remarkable capabilities in transforming textual prompts into coherent images, yet the computational cost of their inference remains a persistent challenge.

Text-to-Image Generation

Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images

no code implementations10 Nov 2023 Shouyue Liu, Jinkui Hao, Yanwu Xu, Huazhu Fu, Xinyu Guo, Jiang Liu, Yalin Zheng, Yonghuai Liu, Jiong Zhang, Yitian Zhao

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature.

Alzheimer's Disease Detection Decision Making

CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation

no code implementations6 Oct 2023 Weibin Liao, Xuhong LI, Qingzhong Wang, Yanwu Xu, Zhaozheng Yin, Haoyi Xiong

While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on massive fine-annotated cell images [2] with bounding boxes, masks, and cell types for every cell in every image, to fine-tune the pre-trained model.

Cell Segmentation Contrastive Learning +6

MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

no code implementations5 Oct 2023 Yanwu Xu, Li Sun, Wei Peng, Shyam Visweswaran, Kayhan Batmanghelich

This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements.

Anatomy Image Generation +1

A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning

2 code implementations2 Sep 2023 Heng Li, Haofeng Liu, Huazhu Fu, Yanwu Xu, Hui Shu, Ke Niu, Yan Hu, Jiang Liu

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems.

Image Enhancement Representation Learning

CLIP3Dstyler: Language Guided 3D Arbitrary Neural Style Transfer

no code implementations25 May 2023 Ming Gao, Yanwu Xu, Yang Zhao, Tingbo Hou, Chenkai Zhao, Mingming Gong

In this paper, we propose a novel language-guided 3D arbitrary neural style transfer method (CLIP3Dstyler).

Style Transfer

PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation

1 code implementation13 May 2023 Huihui Fang, Fei Li, Junde Wu, Huazhu Fu, Xu sun, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu

Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment.

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

3 code implementations25 Apr 2023 Junde Wu, Wei Ji, Yuanpei Liu, Huazhu Fu, Min Xu, Yanwu Xu, Yueming Jin

In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation.

Image Segmentation Medical Image Segmentation +2

Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions

1 code implementation6 Apr 2023 Yu Zhang, Xiaoguang Di, Junde Wu, Rao Fu, Yong Li, Yue Wang, Yanwu Xu, Guohui YANG, Chunhui Wang

In this paper, to make the learning easier in low-light image enhancement, we introduce FLW-Net (Fast and LightWeight Network) and two relative loss functions.

Low-Light Image Enhancement

Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints

1 code implementation13 Mar 2023 Shuhan LI, Dong Zhang, Xiaomeng Li, Chubin Ou, Lin An, Yanwu Xu, Kwang-Ting Cheng

In this paper, we propose a novel framework, TransPro, that translates 3D Optical Coherence Tomography (OCT) images into exclusive 3D OCTA images using an image translation pattern.


Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks

no code implementations24 Feb 2023 Yuxuan Zhang, Qingzhong Wang, Jiang Bian, Yi Liu, Yanwu Xu, Dejing Dou, Haoyi Xiong

Due to the high similarity between MRI data and videos, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification?

Classification Data Augmentation +3

MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer

2 code implementations19 Jan 2023 Junde Wu, Wei Ji, Huazhu Fu, Min Xu, Yueming Jin, Yanwu Xu

To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2.

Image Generation Image Segmentation +3

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters

no code implementations1 Dec 2022 Junde Wu, Huihui Fang, Yehui Yang, Yuanpei Liu, Jing Gao, Lixin Duan, Weihua Yang, Yanwu Xu

In this paper, we propose a novel neural network framework, called Multi-Rater Prism (MrPrism) to learn the medical image segmentation from multiple labels.

Image Segmentation Medical Image Segmentation +2

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

2 code implementations1 Nov 2022 Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, Yanwu Xu

Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.

Anomaly Detection Brain Tumor Segmentation +8

Learning to screen Glaucoma like the ophthalmologists

no code implementations23 Sep 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Yanwu Xu

GAMMA Challenge is organized to encourage the AI models to screen the glaucoma from a combination of 2D fundus image and 3D optical coherence tomography volume, like the ophthalmologists.

Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification

no code implementations25 Aug 2022 Zhen Qiu, Yifan Zhang, Fei Li, Xiulan Zhang, Yanwu Xu, Mingkui Tan

Based on these domain-invariant features at different scales, the deep model trained on the source domain is able to classify angle closure on multiple target domains even without any annotations in these domains.

Domain Adaptation Multi-target Domain Adaptation

Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

2 code implementations5 Aug 2022 Junde Wu, Huihui Fang, Hoayi Xiong, Lixin Duan, Mingkui Tan, Weihua Yang, Huiying Liu, Yanwu Xu

Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty.

Image Segmentation Lesion Segmentation +3

Dataset and Evaluation algorithm design for GOALS Challenge

no code implementations29 Jul 2022 Huihui Fang, Fei Li, Huazhu Fu, Junde Wu, Xiulan Zhang, Yanwu Xu

Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma. OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures.


Towards Lightweight Super-Resolution with Dual Regression Learning

2 code implementations16 Jul 2022 Yong Guo, Jingdong Wang, Qi Chen, JieZhang Cao, Zeshuai Deng, Yanwu Xu, Jian Chen, Mingkui Tan

Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space.

Image Super-Resolution Model Compression +1

Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation

no code implementations28 Jun 2022 Yanwu Xu, Shaoan Xie, Maxwell Reynolds, Matthew Ragoza, Mingming Gong, Kayhan Batmanghelich

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models.

Contrastive Learning Domain Generalization +5

SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer

no code implementations12 Jun 2022 Junde Wu, Huihui Fang, Fangxin Shang, Dalu Yang, Zhaowei Wang, Jing Gao, Yehui Yang, Yanwu Xu

To model the segmentation-diagnosis interaction, SeA-block first embeds the diagnosis feature based on the segmentation information via the encoder, and then transfers the embedding back to the diagnosis feature space by a decoder.

Melanoma Diagnosis Segmentation

Learning self-calibrated optic disc and cup segmentation from multi-rater annotations

1 code implementation10 Jun 2022 Junde Wu, Huihui Fang, Fangxin Shang, Zhaowei Wang, Dalu Yang, Wenshuo Zhou, Yehui Yang, Yanwu Xu

In this paper, we propose a novel neural network framework to learn OD/OC segmentation from multi-rater annotations.


One Hyper-Initializer for All Network Architectures in Medical Image Analysis

no code implementations8 Jun 2022 Fangxin Shang, Yehui Yang, Dalu Yang, Junde Wu, Xiaorong Wang, Yanwu Xu

Pre-training is essential to deep learning model performance, especially in medical image analysis tasks where limited training data are available.

Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification

no code implementations31 May 2022 Wenshuo Zhou, Dalu Yang, Binghong Wu, Yehui Yang, Junde Wu, Xiaorong Wang, Lei Wang, Haifeng Huang, Yanwu Xu

Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc.

domain classification Domain Generalization +3

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

no code implementations16 Feb 2022 Huihui Fang, Fei Li, Huazhu Fu, Xu sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching-Wei Wang, Shih-Chang Lo, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu, iChallenge-AMD study group

The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.

Opinions Vary? Diagnosis First!

1 code implementation14 Feb 2022 Junde Wu, Huihui Fang, Dalu Yang, Zhaowei Wang, Wenshuo Zhou, Fangxin Shang, Yehui Yang, Yanwu Xu

Motivated by the observation that OD/OC segmentation is often used for the glaucoma diagnosis clinically, in this paper, we propose a novel strategy to fuse the multi-rater OD/OC segmentation labels via the glaucoma diagnosis performance.

Medical Image Segmentation Segmentation +1

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

Unaligned Image-to-Image Translation by Learning to Reweight

1 code implementation ICCV 2021 Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang

An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e. g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain.

Translation Unsupervised Image-To-Image Translation

Progressive Hard-case Mining across Pyramid Levels for Object Detection

1 code implementation15 Sep 2021 Binghong Wu, Yehui Yang, Dalu Yang, Junde Wu, Xiaorong Wang, Haifeng Huang, Lei Wang, Yanwu Xu

Based on focal loss with ATSS-R50, our approach achieves 40. 5 AP, surpassing the state-of-the-art QFL (Quality Focal Loss, 39. 9 AP) and VFL (Varifocal Loss, 40. 1 AP).

object-detection Object Detection

Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision

no code implementations19 Aug 2021 Yanwu Xu, Mingming Gong, Shaoan Xie, Kayhan Batmanghelich

In this paper, we propose a weakly supervised do-main adaptation setting, in which we can partially label newdatasets with bounding boxes, which are easier and cheaperto obtain than segmentation masks.

Domain Adaptation Image Segmentation +3

Weighing Features of Lung and Heart Regions for Thoracic Disease Classification

no code implementations26 May 2021 Jiansheng Fang, Yanwu Xu, Yitian Zhao, Yuguang Yan, Junling Liu, Jiang Liu

By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.

Binarization Thoracic Disease Classification

A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation

no code implementations21 May 2021 Yinglin Zhang, Risa Higashita, Huazhu Fu, Yanwu Xu, Yang Zhang, Haofeng Liu, Jian Zhang, Jiang Liu

Corneal endothelial cell segmentation plays a vital role inquantifying clinical indicators such as cell density, coefficient of variation, and hexagonality.

Cell Segmentation

Attention-based Saliency Hashing for Ophthalmic Image Retrieval

1 code implementation7 Dec 2020 Jiansheng Fang, Yanwu Xu, Xiaoqing Zhang, Yan Hu, Jiang Liu

The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions.

Deep Hashing Image Retrieval

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

1 code implementation7 Dec 2020 Jiansheng Fang, Xiaoqing Zhang, Yan Hu, Yanwu Xu, Ming Yang, Jiang Liu

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.

Bayesian Inference Collaborative Filtering +1

Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN

1 code implementation5 Aug 2020 Li Sun, Junxiang Chen, Yanwu Xu, Mingming Gong, Ke Yu, Kayhan Batmanghelich

During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image.

Data Augmentation Domain Adaptation +4

Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening

no code implementations31 Jul 2020 Dalu Yang, Yehui Yang, Tiantian Huang, Binghong Wu, Lei Wang, Yanwu Xu

How can we train a classification model on labeled fundus images ac-quired from only one camera brand, yet still achieves good performance on im-ages taken by other brands of cameras?

Classification Domain Adaptation +1

Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

1 code implementation31 Jul 2020 Xu Sun, Huihui Fang, Yehui Yang, Dongwei Zhu, Lei Wang, Junwei Liu, Yanwu Xu

In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.

Data Augmentation Retinal Vessel Segmentation

Open-Narrow-Synechiae Anterior Chamber Angle Classification in AS-OCT Sequences

no code implementations9 Jun 2020 Huaying Hao, Huazhu Fu, Yanwu Xu, Jianlong Yang, Fei Li, Xiulan Zhang, Jiang Liu, Yitian Zhao

However, clinical diagnosis requires a more discriminating ACA three-class system (i. e., open, narrow, or synechiae angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types.

Binary Classification General Classification

Twin Auxilary Classifiers GAN

1 code implementation NeurIPS 2019 Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich

One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.

Conditional Image Generation

Attention Guided Network for Retinal Image Segmentation

2 code implementations25 Jul 2019 Shihao Zhang, Huazhu Fu, Yuguang Yan, Yubing Zhang, Qingyao Wu, Ming Yang, Mingkui Tan, Yanwu Xu

Learning structural information is critical for producing an ideal result in retinal image segmentation.

Image Segmentation Segmentation +1

Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

4 code implementations10 Jul 2019 Huazhu Fu, Boyang Wang, Jianbing Shen, Shanshan Cui, Yanwu Xu, Jiang Liu, Ling Shao

Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems.

Image Quality Assessment

Twin Auxiliary Classifiers GAN

4 code implementations5 Jul 2019 Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich

One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.

Conditional Image Generation

Generative-Discriminative Complementary Learning

no code implementations2 Apr 2019 Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich

The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases.

You Only Look & Listen Once: Towards Fast and Accurate Visual Grounding

no code implementations12 Feb 2019 Chaorui Deng, Qi Wu, Guanghui Xu, Zhuliang Yu, Yanwu Xu, Kui Jia, Mingkui Tan

Most state-of-the-art methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem that finds the best match between the language query and all region proposals.

object-detection Object Detection +2

Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network

no code implementations10 Feb 2019 Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Mani Baskaran, Meenakshi Mahesh, Tin Aung, Jiang Liu

A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch.

Robust Angular Local Descriptor Learning

1 code implementation21 Jan 2019 Yanwu Xu, Mingming Gong, Tongliang Liu, Kayhan Batmanghelich, Chaohui Wang

In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-Net [1] and triplet based metric learning [2].

Metric 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

Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss

no code implementations19 Sep 2018 Yong Guo, Qi Chen, Jian Chen, Junzhou Huang, Yanwu Xu, JieZhang Cao, Peilin Zhao, Mingkui Tan

However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.

Image Super-Resolution

Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT

no code implementations10 Sep 2018 Huazhu Fu, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Baskaran Mani, Meenakshi Mahesh, Tin Aung, Jiang Liu

A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT).

General Classification

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

3 code implementations19 May 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

3 code implementations3 Jan 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function.


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