Search Results for author: Linlin Shen

Found 60 papers, 20 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 Image-to-Image Translation +2

Delving into the Scale Variance Problem in Object Detection

no code implementations16 Jun 2022 Junliang Chen, Xiaodong Zhao, Linlin Shen

For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2. 5\% improvement in AP (on COCO 2017 dataset), with only 3\% increase of FLOPs.

object-detection Object Detection

Selective Multi-Scale Learning for Object Detection

no code implementations16 Jun 2022 Junliang Chen, Weizeng Lu, Linlin Shen

When integrated with SMSL, two-stage detectors can get around 1. 0\% improvement in AP.

object-detection Object Detection

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.

OOD Detection

Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition

1 code implementation2 May 2022 Cheng Luo, Siyang Song, Weicheng Xie, Linlin Shen, Hatice Gunes

While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display.

Facial Action Unit Detection

Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

2 code implementations25 Mar 2022 Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, Linlin Shen

While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.

Contrastive Learning Image Classification +2

Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity

1 code implementation CVPR 2022 Cheng Luo, Qinliang Lin, Weicheng Xie, Bizhu Wu, Jinheng Xie, Linlin Shen

Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations.

Adversarial Attack Semantic Similarity +1

Cross Language Image Matching for Weakly Supervised Semantic Segmentation

2 code implementations5 Mar 2022 Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen

As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.

Weakly-Supervised Semantic Segmentation

FEAT: Face Editing with Attention

no code implementations6 Feb 2022 Xianxu Hou, Linlin Shen, Or Patashnik, Daniel Cohen-Or, Hui Huang

In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.


CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation

no code implementations CVPR 2022 Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen

As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects.

Weakly-Supervised Semantic Segmentation

C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

no code implementations CVPR 2022 Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, Linlin Shen

While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions.

Contrastive Learning Image Classification +2

Gated SwitchGAN for multi-domain facial image translation

no code implementations28 Nov 2021 Xiaokang Zhang, Yuanlue Zhu, WenTing Chen, Wenshuang Liu, Linlin Shen

The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation.

feature selection Translation

Face Presentation Attack Detection using Taskonomy Feature

no code implementations22 Nov 2021 Wentian Zhang, Haozhe Liu, Raghavendra Ramachandra, Feng Liu, Linlin Shen, Christoph Busch

The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs).

Face Presentation Attack Detection Face Recognition +1

Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

no code implementations15 Nov 2021 Feng Liu, Zhe Kong, Haozhe Liu, Wentian Zhang, Linlin Shen

Besides, our model is simpler, lighter and, more efficient and has achieved a 74. 76% reduction in time-consuming compared with the state-of-the-art multiple model based method.


Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition

no code implementations26 Oct 2021 Siyang Song, Zilong Shao, Shashank Jaiswal, Linlin Shen, Michel Valstar, Hatice Gunes

This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours.

Neural Architecture Search

Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization

1 code implementation ICCV 2021 Jinheng Xie, Cheng Luo, Xiangping Zhu, Ziqi Jin, Weizeng Lu, Linlin Shen

In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner.

Weakly-Supervised Object Localization

Manifold-preserved GANs

no code implementations18 Sep 2021 Haozhe Liu, Hanbang Liang, Xianxu Hou, Haoqian Wu, Feng Liu, Linlin Shen

Generative Adversarial Networks (GANs) have been widely adopted in various fields.

Taming Self-Supervised Learning for Presentation Attack Detection: In-Image De-Folding and Out-of-Image De-Mixing

no code implementations9 Sep 2021 Haozhe Liu, Zhe Kong, Raghavendra Ramachandra, Feng Liu, Linlin Shen, Christoph Busch

Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.

Self-Supervised Learning

WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification

2 code implementations28 Jul 2021 Qiufu Li, Linlin Shen, Sheng Guo, Zhihui Lai

We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet).

Adversarial Robustness Image Classification

Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis

no code implementations23 Jun 2021 Zhongliang Li, Zhihao Jin, Xuechen Li, Linlin Shen

The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data.

Computed Tomography (CT) COVID-19 Diagnosis +2

Self-Attention Based Text Knowledge Mining for Text Detection

1 code implementation CVPR 2021 Qi Wan, Haoqin Ji, Linlin Shen

Considering the importance of exploring text contents for text detection, we propose STKM (Self-attention based Text Knowledge Mining), which consists of a CNN Encoder and a Self-attention Decoder, to learn general prior knowledge for text detection from SynthText.

Neuron segmentation using 3D wavelet integrated encoder-decoder network

1 code implementation1 Jun 2021 Qiufu Li, Linlin Shen

Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers.

Learning a Model-Driven Variational Network for Deformable Image Registration

no code implementations25 May 2021 Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B Styles, Hyung Jin Chang, Ales Leonardis, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Jinming Duan

We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).

Denoising Image Registration

Group-wise Inhibition based Feature Regularization for Robust Classification

1 code implementation ICCV 2021 Haozhe Liu, Haoqian Wu, Weicheng Xie, Feng Liu, Linlin Shen

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).

Classification Domain Generalization +2

MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

1 code implementation26 Feb 2021 Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, Yefeng Zheng

Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.

Medical Image Segmentation Semantic Segmentation

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

3 code implementations24 Feb 2021 Bing Li, Yuanlue Zhu, Yitong Wang, Chia-Wen Lin, Bernard Ghanem, Linlin Shen

Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face.

Face Generation Translation

GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing

no code implementations22 Dec 2020 Xianxu Hou, Xiaokang Zhang, Linlin Shen, Zhihui Lai, Jun Wan

Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing.

Image Generation

HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation

no code implementations12 Dec 2020 Saqib Qamar, Parvez Ahmad, Linlin Shen

Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0. 79457, 0. 87494, and 0. 83712, respectively.

Brain Tumor Segmentation Decision Making +1

Robust Facial Landmark Detection by Cross-order Cross-semantic Deep Network

no code implementations16 Nov 2020 Jun Wan, Zhihui Lai, Linlin Shen, Jie zhou, Can Gao, Gang Xiao, Xianxu Hou

Moreover, a novel cross-order cross-semantic (COCS) regularizer is designed to drive the network to learn cross-order cross-semantic features from different activation for facial landmark detection.

Facial Landmark Detection

Context Aware 3D UNet for Brain Tumor Segmentation

no code implementations25 Oct 2020 Parvez Ahmad, Saqib Qamar, Linlin Shen, Adnan Saeed

UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation.

Brain Tumor Segmentation Tumor Segmentation

Think about boundary: Fusing multi-level boundary information for landmark heatmap regression

no code implementations25 Aug 2020 Jinheng Xie, Jun Wan, Linlin Shen, Zhihui Lai

Although current face alignment algorithms have obtained pretty good performances at predicting the location of facial landmarks, huge challenges remain for faces with severe occlusion and large pose variations, etc.

Face Alignment

Translate the Facial Regions You Like Using Region-Wise Normalization

no code implementations29 Jul 2020 Wenshuang Liu, Wenting Chen, Linlin Shen

We propose in this paper a region-wise normalization framework, for region level face translation.

Image Generation Translation

TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification

no code implementations29 Jul 2020 Wenting Chen, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Chunyan Chu, Linlin Shen, Yefeng Zheng

A topology ranking discriminator based on ordinal regression is proposed to rank the topological connectivity level of the ground-truth, the generated A/V mask and the intentionally shuffled mask.

Classification General Classification

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 Translation

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 Self-Supervised Learning +1

Geometry Constrained Weakly Supervised Object Localization

1 code implementation ECCV 2020 Weizeng Lu, Xi Jia, Weicheng Xie, Linlin Shen, Yicong Zhou, Jinming Duan

The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.

Weakly-Supervised Object Localization

WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation

no code implementations29 May 2020 Qiufu Li, Linlin Shen

In deep networks, the lost data details significantly degrade the performances of image segmentation.

Semantic Segmentation

Wavelet Integrated CNNs for Noise-Robust Image Classification

1 code implementation CVPR 2020 Qiufu Li, Linlin Shen, Sheng Guo, Zhihui Lai

The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets.

Classification General Classification +1

Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks

no code implementations5 Apr 2020 Pourya Shamsolmoali, Masoumeh Zareapoor, Linlin Shen, Abdul Hamid Sadka, Jie Yang

It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples.

Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

no code implementations4 Jun 2019 Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu

We present a new method for improving the performances of variational autoencoder (VAE).

Inferring Dynamic Representations of Facial Actions from a Still Image

no code implementations4 Apr 2019 Siyang Song, Enrique Sánchez-Lozano, Linlin Shen, Alan Johnston, Michel Valstar

We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation.

Texture Deformation Based Generative Adversarial Networks for Face Editing

no code implementations24 Dec 2018 WenTing Chen, Xinpeng Xie, Xi Jia, Linlin Shen

We also evaluate our approach qualitatively and quantitatively on facial attribute and facial expression synthesis.

Image-to-Image Translation Translation

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

A Self-Organizing Tensor Architecture for Multi-View Clustering

no code implementations18 Oct 2018 Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.

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 General Classification +1

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

Kernelized Support Tensor Machines

no code implementations ICML 2017 Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.

Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification

no code implementations CVPR 2017 Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.

Classification General Classification

Skin Lesion Classification using Class Activation Map

no code implementations3 Mar 2017 Xi Jia, Linlin Shen

We proposed a two stage framework with only one network to analyze skin lesion images, we firstly trained a convolutional network to classify these images, and cropped the import regions which the network has the maximum activation value.

Classification General Classification +2

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 +1

Deep Feature Consistent Variational Autoencoder

13 code implementations2 Oct 2016 Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu

We present a novel method for constructing Variational Autoencoder (VAE).

Style Transfer

Object Specific Deep Learning Feature and Its Application to Face Detection

no code implementations6 Sep 2016 Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu

We present a method for discovering and exploiting object specific deep learning features and use face detection as a case study.

Face Detection

Latent Constrained Correlation Filters for Object Localization

no code implementations7 Jun 2016 Shangzhen Luan, Baochang Zhang, Jungong Han, Chen Chen, Ling Shao, Alessandro Perina, Linlin Shen

There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.

Object Localization

LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification

no code implementations22 Apr 2015 Xianbiao Qi, Guoying Zhao, Linlin Shen, Qingquan Li, Matti Pietikainen

It is worth to mention that we achieve a 65. 4\% classification accuracy-- which is, to the best of our knowledge, the highest record by far --on Flickr Material Database by using a single feature.

General Classification Material Classification +2

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