Search Results for author: Yuexiang Li

Found 50 papers, 25 papers with code

Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

no code implementations ECCV 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Recent generative adversarial network (GAN) based methods (e. g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation.

Domain Adaptation Generative Adversarial Network +4

Dual Adversarial Network for Deep Active Learning

no code implementations ECCV 2020 Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng

In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.

Active Learning

Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

no code implementations2 Jan 2024 Zhe Kong, Wentian Zhang, Tao Wang, Kaihao Zhang, Yuexiang Li, Xiaoying Tang, Wenhan Luo

In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment.

Adversarial Attack Face Anti-Spoofing +2

Adversarial Medical Image with Hierarchical Feature Hiding

1 code implementation4 Dec 2023 Qingsong Yao, Zecheng He, Yuexiang Li, Yi Lin, Kai Ma, Yefeng Zheng, S. Kevin Zhou

Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs.

Decision Making

Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

1 code implementation22 Sep 2023 Dong Wei, Yawen Huang, Donghuan Lu, Yuexiang Li, Yefeng Zheng

Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers.

Anatomy

Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction

no code implementations20 Aug 2023 Yunlu Yan, Chun-Mei Feng, Yuexiang Li, Rick Siow Mong Goh, Lei Zhu

In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction.

Federated Learning MRI Reconstruction

BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion

2 code implementations ICCV 2023 Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, Mike Zheng Shou

As such paired data is time-consuming and labor-intensive to acquire and restricted to a closed set, this potentially becomes the bottleneck for applications in an open world.

Conditional Text-to-Image Synthesis Denoising

Dynamically Masked Discriminator for Generative Adversarial Networks

1 code implementation13 Jun 2023 Wentian Zhang, Haozhe Liu, Bing Li, Jinheng Xie, Yawen Huang, Yuexiang Li, Yefeng Zheng, Bernard Ghanem

By treating the generated data in training as a stream, we propose to detect whether the discriminator slows down the learning of new knowledge in generated data.

Continual Learning

Cross-Modal Vertical Federated Learning for MRI Reconstruction

no code implementations5 Jun 2023 Yunlu Yan, Hong Wang, Yawen Huang, Nanjun He, Lei Zhu, Yuexiang Li, Yong Xu, Yefeng Zheng

To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals.

Disentanglement MRI Reconstruction +1

VisorGPT: Learning Visual Prior via Generative Pre-Training

1 code implementation23 May 2023 Jinheng Xie, Kai Ye, Yudong Li, Yuexiang Li, Kevin Qinghong Lin, Yefeng Zheng, Linlin Shen, Mike Zheng Shou

Experimental results demonstrate that VisorGPT can effectively model the visual prior, which can be employed for many vision tasks, such as customizing accurate human pose for conditional image synthesis models like ControlNet.

Image Generation Language Modelling +1

Open-World Weakly-Supervised Object Localization

1 code implementation17 Apr 2023 Jinheng Xie, Zhaochuan Luo, Yuexiang Li, Haozhe Liu, Linlin Shen, Mike Zheng Shou

To handle such data, we propose a novel paradigm of contrastive representation co-learning using both labeled and unlabeled data to generate a complete G-CAM (Generalized Class Activation Map) for object localization, without the requirement of bounding box annotation.

Object Representation Learning +1

Improving GAN Training via Feature Space Shrinkage

1 code implementation2 Mar 2023 Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng

The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.

Out of Distribution (OOD) Detection

Interactive Segmentation as Gaussian Process Classification

1 code implementation28 Feb 2023 Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng

Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.

Binary Classification Classification +4

FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs

1 code implementation ICCV 2023 Peng Tu, Xu Xie, Guo Ai, Yuexiang Li, Yawen Huang, Yefeng Zheng

Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors.

object-detection Object Detection

A New Perspective to Boost Vision Transformer for Medical Image Classification

no code implementations3 Jan 2023 Yuexiang Li, Yawen Huang, Nanjun He, Kai Ma, Yefeng Zheng

The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Diabetic Retinopathy Grading Image Classification +5

AdaptiveMix: Improving GAN Training via Feature Space Shrinkage

1 code implementation CVPR 2023 Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng

The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.

Out of Distribution (OOD) Detection

SemiCVT: Semi-Supervised Convolutional Vision Transformer for Semantic Segmentation

no code implementations CVPR 2023 Huimin Huang, Shiao Xie, Lanfen Lin, Ruofeng Tong, Yen-Wei Chen, Yuexiang Li, Hong Wang, Yawen Huang, Yefeng Zheng

Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.

Semantic Segmentation

Interactive Segmentation As Gaussion Process Classification

1 code implementation CVPR 2023 Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng

Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.

Binary Classification Classification +4

Orientation-Shared Convolution Representation for CT Metal Artifact Learning

1 code implementation26 Dec 2022 Hong Wang, Qi Xie, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng

During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment.

Computed Tomography (CT) Metal Artifact Reduction

Decoupled Mixup for Generalized Visual Recognition

1 code implementation26 Oct 2022 Haozhe Liu, Wentian Zhang, Jinheng Xie, Haoqian Wu, Bing Li, Ziqi Zhang, Yuexiang Li, Yawen Huang, Bernard Ghanem, Yefeng Zheng

Since the observation is that noise-prone regions such as textural and clutter backgrounds are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair.

A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays

1 code implementation5 Sep 2022 Haoqin Ji, Haozhe Liu, Yuexiang Li, Jinheng Xie, Nanjun He, Yawen Huang, Dong Wei, Xinrong Chen, Linlin Shen, Yefeng Zheng

Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.

Combating Mode Collapse in GANs via Manifold Entropy Estimation

1 code implementation25 Aug 2022 Haozhe Liu, Bing Li, Haoqian Wu, Hanbang Liang, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng

In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs.

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

1 code implementation6 Jun 2022 Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, Yefeng Zheng

Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.

Brain Tumor Segmentation Segmentation +1

Robust Representation via Dynamic Feature Aggregation

1 code implementation16 May 2022 Haozhe Liu, Haoqin Ji, Yuexiang Li, Nanjun He, Haoqian Wu, Feng Liu, Linlin Shen, Yefeng Zheng

With the regularization and orthogonal classifier, a more compact embedding space can be obtained, which accordingly improves the model robustness against adversarial attacks.

Out of Distribution (OOD) Detection

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

1 code implementation16 May 2022 Hong Wang, Yuexiang Li, Deyu Meng, Yefeng Zheng

By unfolding every iterative substep of the proposed algorithm into a network module, we explicitly embed the prior structure into a deep network, \emph{i. e.,} a clear interpretability for the MAR task.

Computed Tomography (CT) Metal Artifact Reduction

DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

no code implementations12 Mar 2022 Heqin Zhu, Xu sun, Yuexiang Li, Kai Ma, S. Kevin Zhou, Yefeng Zheng

This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.

Benchmarking Object +3

Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images

1 code implementation4 Mar 2022 Hong Liu, Dong Wei, Donghuan Lu, Yuexiang Li, Kai Ma, Liansheng Wang, Yefeng Zheng

To the best of our knowledge, this is the first study that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs.

Segmentation

Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training

1 code implementation16 Feb 2022 Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm.

Representation Learning Segmentation +2

Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship

no code implementations31 Dec 2021 Quanziang Wang, Renzhen Wang, Yuexiang Li, Dong Wei, Kai Ma, Yefeng Zheng, Deyu Meng

Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data.

Continual Learning Meta-Learning

InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images

1 code implementation23 Dec 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Meng, Yefeng Zheng

To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded.

Computed Tomography (CT) Metal Artifact Reduction

Alleviating Noisy-label Effects in Image Classification via Probability Transition Matrix

no code implementations17 Oct 2021 Ziqi Zhang, Yuexiang Li, Hongxin Wei, Kai Ma, Tao Xu, Yefeng Zheng

The hard samples, which are beneficial for classifier learning, are often mistakenly treated as noises in such a setting since both the hard samples and ones with noisy labels lead to a relatively larger loss value than the easy cases.

Image Classification

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction

1 code implementation11 Sep 2021 Hong Wang, Yuexiang Li, Haimiao Zhang, Jiawei Chen, Kai Ma, Deyu Meng, Yefeng Zheng

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration.

Metal Artifact Reduction

RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining

1 code implementation14 Jul 2021 Hong Wang, Qi Xie, Qian Zhao, Yuexiang Li, Yong Liang, Yefeng Zheng, Deyu Meng

To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability.

Single Image Deraining

Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint

no code implementations30 Jun 2021 Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

In practical applications, the outdoor weather and illumination are changeable, e. g., cloudy and nighttime, which results in a significant drop of semantic segmentation accuracy of CNN only trained with daytime data.

Autonomous Driving Generative Adversarial Network +4

Residual Moment Loss for Medical Image Segmentation

no code implementations27 Jun 2021 Quanziang Wang, Renzhen Wang, Yuexiang Li, Kai Ma, Yefeng Zheng, Deyu Meng

Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.

Image Segmentation Medical Image Segmentation +2

Aggregative Self-Supervised Feature Learning from a Limited Sample

no code implementations14 Dec 2020 Jiuwen Zhu, Yuexiang Li, S. Kevin Zhou

Then, in self-aggregative SSL, we propose to self-complement an existing proxy task with an auxiliary loss function based on a linear centered kernel alignment metric, which explicitly promotes the exploring of where are uncovered by the features learned from a proxy task at hand to further boost the modeling capability.

Image Classification Medical Image Classification +1

Instance-aware Self-supervised Learning for Nuclei Segmentation

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.

Instance Segmentation Segmentation +2

MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint

no code implementations22 Jul 2020 Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models.

Domain Adaptation Generative Adversarial Network +3

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation

no code implementations20 Jul 2020 Yuexiang Li, Jia-Wei Chen, Xinpeng Xie, Kai Ma, Yefeng Zheng

A novel pseudo-label (namely self-loop uncertainty), generated by recurrently optimizing the neural network with a self-supervised task, is adopted as the ground-truth for the unlabeled images to augment the training set and boost the segmentation accuracy.

Image Segmentation Pseudo Label +3

Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning

no code implementations10 Jun 2020 Jiuwen Zhu, Yuexiang Li, Yifan Hu, S. Kevin Zhou

To this end, self-supervised learning (SSL), as a potential solution for deficient annotated data, attracts increasing attentions from the community.

Clustering General Classification +3

Generative Adversarial Networks for Video-to-Video Domain Adaptation

no code implementations17 Apr 2020 Jiawei Chen, Yuexiang Li, Kai Ma, Yefeng Zheng

Two colonoscopic datasets from different centres, i. e., CVC-Clinic and ETIS-Larib, are adopted to evaluate the performance of domain adaptation of our VideoGAN.

Domain Adaptation Generative Adversarial Network +1

Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube

no code implementations5 Oct 2019 Xinrui Zhuang, Yuexiang Li, Yifan Hu, Kai Ma, Yujiu Yang, Yefeng Zheng

Witnessed the development of deep learning, increasing number of studies try to build computer aided diagnosis systems for 3D volumetric medical data.

Brain Tumor Segmentation Rubik's Cube +2

OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images

no code implementations5 Jun 2019 Yu Chen, Jia-Wei Chen, Dong Wei, Yuexiang Li, Yefeng Zheng

Two approaches are widely used in the literature to fuse multiple modalities in the segmentation networks: early-fusion (which stacks multiple modalities as different input channels) and late-fusion (which fuses the segmentation results from different modalities at the very end).

Segmentation

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

Reversed Active Learning based Atrous DenseNet for Pathological Image Classification

no code implementations6 Jul 2018 Yuexiang Li, Xinpeng Xie, Linlin Shen, Shaoxiong Liu

However, the usage of deep learning networks for the pathological image analysis encounters several challenges, e. g. high resolution (gigapixel) of pathological images and lack of annotations of cancer areas.

Active Learning Classification +2

Active Learning for Breast Cancer Identification

no code implementations18 Apr 2018 Xinpeng Xie, Yuexiang Li, Linlin Shen

Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images.

Active Learning

Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network

no code implementations2 Mar 2017 Yuexiang Li, Linlin Shen

In this paper, we proposed two deep learning methods to address all the three tasks announced in ISIC 2017, i. e. lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3).

General Classification Lesion Classification +2

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