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Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images

Computer Vision – ECCV 2018, p.732-750

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Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Computer Vision – ECCV 2016, p.205-220

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Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound

Computer vision and image understanding, 2017-11, Vol.164, p.92-102

2017

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Human skeleton tracking from depth data using geodesic distances and optical flow

Image and vision computing, 2012-03, Vol.30 (3), p.217-226

2012

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Towards Fully Automatic X-Ray to CT Registration

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, p.631-639

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Joint motion boundary detection and CNN-based feature visualization for video object segmentation

Neural computing & applications, 2020-04, Vol.32 (8), p.4073-4091

2020

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Recognizing multiple human activities and tracking full-body pose in unconstrained environments

Pattern recognition, 2012, Vol.45 (1), p.11-23

2012

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Technical Demonstration on Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes

Computer Vision – ECCV 2012. Workshops and Demonstrations, p.593-596

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GANs for medical image analysis

Artificial intelligence in medicine, 2020-09, Vol.109, p.101938-101938, Article 101938

2020

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U-Net: Convolutional Networks for Biomedical Image Segmentation

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, p.234-241

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Peeking behind objects: Layered depth prediction from a single image

Pattern recognition letters, 2019-07, Vol.125, p.333-340

2019

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DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, p.556-564

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Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 2013, Vol.16 (Pt 2), p.246-253

2013

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Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 2013, Vol.16 (Pt 2), p.649-656

2013

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Generating Highly Realistic Images of Skin Lesions with GANs

OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, p.260-267

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A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 2013, Vol.16 (Pt 2), p.403-410

2013

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Inverse Distance Aggregation for Federated Learning with Non-IID Data

Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, p.150-159

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Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 2015-10, Vol.9351, p.374-382

2015

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Deep Residual Learning for Instrument Segmentation in Robotic Surgery

Machine Learning in Medical Imaging, 2019, p.566-573

2019

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Automated Separation of Binary Overlapping Trees in Low-Contrast Color Retinal Images

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 2013, Vol.16 (Pt 2), p.436-443

2013

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FedPerl: Semi-supervised Peer Learning for Skin Lesion Classification

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, p.336-346

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A Global Approach for Automatic Fibroscopic Video Mosaicing in Minimally Invasive Diagnosis

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 2008, Vol.11 (Pt 1), p.850-857

2008

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Deep Model-Based 6D Pose Refinement in RGB

Computer Vision – ECCV 2018, p.833-849

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Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 2013, Vol.16 (Pt 2), p.165-172

2013

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Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, 2015, p.605-612

2015

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Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images

Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound

Human skeleton tracking from depth data using geodesic distances and optical flow

Towards Fully Automatic X-Ray to CT Registration

Joint motion boundary detection and CNN-based feature visualization for video object segmentation

Recognizing multiple human activities and tracking full-body pose in unconstrained environments

Technical Demonstration on Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes

GANs for medical image analysis

U-Net: Convolutional Networks for Biomedical Image Segmentation

Peeking behind objects: Layered depth prediction from a single image

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

Generating Highly Realistic Images of Skin Lesions with GANs

A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

Inverse Distance Aggregation for Federated Learning with Non-IID Data

Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images

Deep Residual Learning for Instrument Segmentation in Robotic Surgery

Automated Separation of Binary Overlapping Trees in Low-Contrast Color Retinal Images

FedPerl: Semi-supervised Peer Learning for Skin Lesion Classification

A Global Approach for Automatic Fibroscopic Video Mosaicing in Minimally Invasive Diagnosis

Deep Model-Based 6D Pose Refinement in RGB

Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images

Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms

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