2. Papers- 医学图像分析.md · 　前进明亮/Medical-Image-Analysis-MIPAV

[toc]
# ==Review and Survey==
- Guest Editorial Deep Learning in Medical Imaging Overview and Future Promise of an Exciting New Technique 2016 [[paper]](http://ieeexplore.ieee.org/document/7463094/)
- Overview of Deep Learning in Medical Imaging 2017 [[paper]](https://link.springer.com/article/10.1007/s12194-017-0406-5)
- A Survey on Deep Learning in Medical Image Analysis 2017 [[paper]](http://www.sciencedirect.com/science/article/pii/S1361841517301135)
- Deep Learning Applications in Medical Image Analysis 2017 [[paper]](https://ieeexplore.ieee.org/document/8241753/)
- Deep Learning in Medical Image Analysis 2017 [[paper]](http://www.annualreviews.org/doi/10.1146/annurev-bioeng-071516-044442)
- Deep Learning in Microscopy Image Analysis A Survey 2017 [[paper]](https://ieeexplore.ieee.org/document/8118310/)
- GANs for Medical Image Analysis 2018 [[paper]](https://arxiv.org/abs/1809.06222)

----------------------------------------------------------------------------------------------------------------------------------------
# Computed Tomography (CT)
## 2015
- 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data MICCAI 2015 [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-24553-9_69)

## 2016
- Low-dose CT Denoising with Convolutional Neural Network [[paper]](https://arxiv.org/abs/1610.00321)
- Low-Dose CT via Deep Neural Network [[paper]](https://arxiv.org/abs/1609.08508)
- Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks [[paper]](https://ieeexplore.ieee.org/document/7422783/)
- Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields MICCAI 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_48)
"CRF"
- An Artificial Agent for Anatomical Landmark Detection in Medical Images MICCAI 2016 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46726-9_27)

## 2017
- Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [[paper]](https://arxiv.org/abs/1708.00961)
- Automatic Liver Segmentation Using an Adversarial Image-to-Image Network MICCAI 2017 [[paper]](https://arxiv.org/abs/1707.08037)
- Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network [[paper]](https://arxiv.org/abs/1708.06453)
- Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT [[paper]](https://arxiv.org/abs/1708.08333)
- Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image [[paepr]](https://arxiv.org/abs/1709.02073)
- A Self-aware Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation [[paper]](https://arxiv.org/abs/1709.02764)
- DeepLesion Automated Deep Mining Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations [[paper]](https://arxiv.org/abs/1710.01766)
- Unsupervised End-to-end Learning for Deformable Medical Image Registration [[paper]](https://arxiv.org/abs/1711.08608)
- DeepLung 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification [[paper]](https://arxiv.org/abs/1709.05538)
- CT Image Denoising with Perceptive Deep Neural Networks [[paper]](https://arxiv.org/abs/1702.07019)
- Improving Low-Dose CT Image Using Residual Convolutional Network [[paper]](http://ieeexplore.ieee.org/document/8082505/)
- Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN) [[paper]](https://ieeexplore.ieee.org/document/7947200/)
- Stacked Competitive Networks for Noise Reduction in Low-dose CT [[paper]](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190069)
- Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network [[paper]](https://arxiv.org/abs/1711.08324)
- Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-42999-1_4)
- Robust Multi-scale Anatomical Landmark Detection in Incomplete 3D-CT Data [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66182-7_23)
- Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans TPAMI 2017 [[paper]](https://ieeexplore.ieee.org/document/8187667/)

## 2018
- DeepLung Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification [[paper]](https://arxiv.org/abs/1801.09555)
- Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans [[paper]](https://arxiv.org/abs/1801.08599)
- Attention U-Net Learning Where to Look for the Pancreas [[paper]](https://arxiv.org/abs/1804.03999)
- Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network [[paper]](https://arxiv.org/abs/1803.00399)
- 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network [[paper]](https://arxiv.org/abs/1802.05656)
- Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network [[paper]](https://ieeexplore.ieee.org/document/8332971/)
- Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising [[paper]](https://arxiv.org/abs/1805.00587)
- Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data MedIA 2018 [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841518304092)
- Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images Arxiv 2018 [[paper]](https://arxiv.org/abs/1807.02908)
"reinforcement learning", "anatomical landmark localization", "aortic valve". "left atrial appendage"
- Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation [[paper]](https://arxiv.org/abs/1808.00739)
- Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network CVPR 2018 [[paper]](https://arxiv.org/abs/1802.09655)
- AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation [[paper]](https://arxiv.org/abs/1808.05238)
- DeepEM Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection MICCAI 2018 [[paper]](https://arxiv.org/abs/1805.05373)
- Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks 2018 [[paper]](https://arxiv.org/abs/1809.02268)
- Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior [[paper]](https://arxiv.org/abs/1804.01307)

----------------------------------------------------------------------------------------------------------------------------------------
# Magnetic Resonance Imaging (MRI)
## 2016
- Medical Image Synthesis with Context-aware Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1612.05362)
- Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-55050-3_8)
- Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks MICCAI 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_51)
- Regressing Heatmaps for Multiple Landmark Localization Using CNNs MICCAI 2016 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46723-8_27)

## 2017
- SegAN Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[paper]](https://arxiv.org/abs/1706.01805)
- Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images [[paper]](https://arxiv.org/abs/1708.01141)
- Deep MR to CT Synthesis using Unpaired Data [[paper]](https://arxiv.org/abs/1708.01155)
- Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT [[paper]](https://arxiv.org/abs/1708.00983)
- 3D Fully Convolutional Networks for Subcortical Segmentation in MRI A Large-scale Study [[paper]](http://www.sciencedirect.com/science/article/pii/S1053811917303324) [[code]](https://github.com/josedolz/LiviaNET)
- 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation [[paper]](https://arxiv.org/abs/1707.09813)
- Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
- Deep Generative Adversarial Networks for Compressed Sensing Automates MRI [[paper]](https://arxiv.org/abs/1706.00051)
- Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation [[paper]](https://arxiv.org/abs/1708.09300)
- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks [[paper]](https://arxiv.org/abs/1709.00382)
- Deep Learning with Domain Adaptation for Accelerated Projection Reconstruction MR [[paper]](https://arxiv.org/abs/1703.01135)
- A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [[paper]](https://ieeexplore.ieee.org/document/8067520/)
- Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1709.00753)
- Learning a Variational Network for Reconstruction of Accelerated MRI Data [[paper]](https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.26977)
- A Parallel MR Imaging Method Using Multilayer Perceptron [[paper]](https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.12600)
- A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [[paper]](https://ieeexplore.ieee.org/document/8067520/)
- Image Reconstruction by Domain Transform Manifold Learning [[paper]](https://arxiv.org/abs/1704.08841)
- Human-level CMR Image Analysis with Deep Fully Convolutional Networks [[paper]](https://arxiv.org/abs/1710.09289)
- A Novel Automatic Segmentation Method to Quantify the Effects of Spinal Cord Injury on Human Thigh Muscles and Adipose Tissue MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_79)
- Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66185-8_49)

## 2018
- Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks [[paper]](https://arxiv.org/abs/1801.02728)
- 3D Multi-scale FCN with Random Modality Voxel Dropout Learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images [[paper]](https://www.sciencedirect.com/science/article/pii/S1361841518300136)
- Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network [[paper]](https://arxiv.org/abs/1803.01417)
- Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks [[paper]](https://arxiv.org/abs/1804.00432)
- k-Space Deep Learning for Accelerated MRI [[paper]](https://arxiv.org/abs/1805.03779)
- Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation [[paper]](https://arxiv.org/abs/1808.01200)
- Deformable Image Registration Using a Cue-Aware Deep Regression Network TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8331111/)
- Multi-Views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8067513/)
- 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes MICCAI 2018 [[paper]](https://arxiv.org/abs/1809.00076)
"focal loss", "Exponential Logarithmic Loss"
- Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks 2018 [[paper]](https://link.springer.com/chapter/10.1007/978-3-662-56537-7_89)
- An Unsupervised Learning Model for Deformable Medical Image Registration CVPR 2018 [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/html/Balakrishnan_An_Unsupervised_Learning_CVPR_2018_paper.html)
- VoxelMorph: A Learning Framework for Deformable Medical Image Registration IEEE TMI 2018 [[paper]](https://arxiv.org/abs/1809.05231)

----------------------------------------------------------------------------------------------------------------------------------------
# Ultrasound (US)
## 2015
- Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-24553-9_62)

## 2016
- Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset [[paper]](https://www.sciencedirect.com/science/article/pii/S0925231216002344)
- Real-time Detection and Localisation of Fetal Standard Scan Planes in 2D Freehand Ultrasound 2016 [[paper]]()
- Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_24)
- Describing Ultrasound Video Content Using Deep Convolutional Neural Networks 2016 [[paper]](http://ieeexplore.ieee.org/document/7493384/)

## 2017
- Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning [[paepr]](https://arxiv.org/abs/1706.00712)
- Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1707.05392)
- Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning [[paper]](https://arxiv.org/abs/1712.07881)
- Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation [[paper]](http://ieeexplore.ieee.org/document/8051114/)
- Hough-CNN Deep learning for segmentation of deep brain regions in MRI and ultrasound CVIU 2017 [[paper]](https://www.sciencedirect.com/science/article/pii/S1077314217300620)
- Cascaded Fully Convolutional Networks for Automatic Prenatal Ultrasound Image Segmentation 2017 [[paper]](http://ieeexplore.ieee.org/document/7950607/)
- Ultrasound Standard Plane Detection Using a Composite Neural Network Framework 2017 [[paper]](http://ieeexplore.ieee.org/document/7890445/)
- CNN-based Estimation of Abdominal Circumference from Ultrasound Images 2017 [[paper]](https://arxiv.org/abs/1702.02741)

## 2018
- Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting [[paper]](https://ieeexplore.ieee.org/document/7927411/)
- Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks [[paper]](https://arxiv.org/abs/1804.09102)
- Cascaded Transforming Multi-task Networks For Abdominal Biometric Estimation from Ultrasound [[paepr]](https://openreview.net/forum?id=r1ZGQW2if)
- Adversarial Image Registration with Application for MR and TRUS Image Fusion 2018 [[paper]](https://arxiv.org/abs/1804.11024)
- Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model 2018 [[paper]](https://ieeexplore.ieee.org/document/8051098/)
- High Frame-rate Cardiac Ultrasound Imaging with Deep Learning MICCAI 2018 [[paper]](https://arxiv.org/abs/1808.07823)
- High Quality Ultrasonic Multi-line Transmission through Deep Learning MICCAI 2018 [[paper]](https://arxiv.org/abs/1808.07819)
- Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging 2018 [[paper]](https://arxiv.org/abs/1805.10737)
- Weakly Supervised Localisation for Fetal Ultrasound Images DLMIAW 2018 [[paper]](https://arxiv.org/abs/1808.00793)
- Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network 2018 [[paper]](https://arxiv.org/abs/1806.07486)
- Less is More Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images 2018 [[paper]](https://arxiv.org/abs/1805.10376)
- Attention-Gated Networks for Improving Ultrasound Scan Plane Detection 2018 [[paper]](https://openreview.net/forum?id=BJtn7-3sM)
- A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8372445/)

----------------------------------------------------------------------------------------------------------------------------------------
# X-ray
## 2015
- Deep Learning and Structured Prediction for the Segmentation of Mass in Mamograms MICCAI 2015 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-24553-9_74)

## 2016
- Learning to Read Chest X-Rays Recurrent Neural Cascade Model for Automated Image Annotation 2016 [[paper]](https://arxiv.org/abs/1603.08486)

## 2017
- Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks [[paper]](https://arxiv.org/abs/1708.00710)
- Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks [[paper]](https://arxiv.org/abs/1705.09850)
- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks 2017 [[paper]](https://arxiv.org/abs/1712.05053)
- Cascade of Multi-scale Convolutional Neural Networks for Bone Suppression of Chest Radiographs in Gradient Domain 2017 [[paper]](http://www.sciencedirect.com/science/article/pii/S1361841516301529)
- CheXNet Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning 2017 [[paper]](https://arxiv.org/abs/1711.05225)
- Adversarial Deep Structural Networks for Mammographic Mass Segmentation MICCAI 2017 [[paper]](https://arxiv.org/abs/1612.05970)
- Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification MICCAI 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-66179-7_69)
- A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification 2017 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-67558-9_20)
- High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks 2017 [[paper]](https://arxiv.org/abs/1703.07047)
- Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning TMI 2017 [[paper]](https://ieeexplore.ieee.org/document/8032490/)

## 2018
- SCAN Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays [[paper]](https://openreview.net/forum?id=HJ1RffhjM)
- Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs TMI 2018 [[paper]](https://ieeexplore.ieee.org/document/8302848/)
- Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation 2018 [[paper]](https://arxiv.org/abs/1806.00600)
- LF-SegNet A Fully Convolutional Encoder–Decoder Network for Segmenting Lung Fields from Chest Radiographs 2018 [[paper]](https://link.springer.com/article/10.1007/s11277-018-5702-9)
- Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks 2018 [[paper]]()
- Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification 2018 [[paper]](https://arxiv.org/abs/1803.02315)
- Breast Mass Segmentation and Shape Classification in Mammograms Using Deep Neural Networks [[paper]](https://arxiv.org/abs/1809.01687)
"conditional generative adversarial networks", "INbreast", "digital database for screening mammography (DDSM)"
- Medical Image Description Using Multi-task-loss CNN 2016 [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-46976-8_13)
- Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification MICCAI 2018 [[paper]](https://arxiv.org/abs/1805.10207)
- Benign and malignant breast tumors classification based on region growing and CNN segmentation ESA 2015 [[paper]](https://www.sciencedirect.com/science/article/pii/S0957417414005594)
- Adversarial Deep Structured Nets for Mass Segmentation from Mammograms ISBI 2018 [[paper]](https://arxiv.org/abs/1710.09288)
- Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net MICCAI 2018 [[paper]](https://arxiv.org/abs/1808.08885)
- Thoracic Disease Identification and Localization with Limited Supervision CVPR 2018 [[paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers_backup/Li_Thoracic_Disease_Identification_CVPR_2018_paper.pdf)
- Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions 2018 [[paper]](https://arxiv.org/abs/1803.07703)
- Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning CBM 2018 [[paper]](https://www.sciencedirect.com/science/article/pii/S0010482518300799)
----------------------------------------------------------------------------------------------------------------------------------------

# PET
## 2017
- Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results [[paper]](https://arxiv.org/abs/1707.09585)
- Combo Loss Handling Input and Output Imbalance in Multi-Organ Segmentation Arxiv 2018 [[paper]](https://arxiv.org/abs/1805.02798)

----------------------------------------------------------------------------------------------------------------------------------------
# Funduscopy
## 2016
- DeepVessel Retinal Vessel Segmentation via Deep Learning and Conditional Random Field MICCAI 2016 [[paper]](http://link.springer.com/chapter/10.1007/978-3-319-46723-8_16)

## 2017
- Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[paper]](https://arxiv.org/abs/1706.09318) [[Keras+TF code]](https://bitbucket.org/woalsdnd/v-gan)

## 2018
- Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation TBME 2018 [[paper]](https://ieeexplore.ieee.org/document/8341481/)
- Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation TMI 2018 [[paper]](http://ieeexplore.ieee.org/document/8252743/)

----------------------------------------------------------------------------------------------------------------------------------------
#  Microscopy
## 2016
- Stain Normalization Using Sparse AutoEncoders (StaNoSA) Application to Digital Pathology [[paper]](http://www.sciencedirect.com/science/article/pii/S0895611116300404)
- Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images [[paper]](http://ieeexplore.ieee.org/document/7163353/)

## 2017
- Adversarial Image Alignment and Interpolation [[paper]](https://arxiv.org/abs/1707.00067)
- CNN Cascades for Segmenting Whole Slide Images of the Kidney [[paper]](https://arxiv.org/abs/1708.00251)
- Learning to Segment Breast Biopsy Whole Slide Images [[paper]](https://arxiv.org/abs/1709.02554)
- SFCN-OPI Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction [[paper]](https://arxiv.org/abs/1712.08297)
- MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network CVPR 2017 [[paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_MDNet_A_Semantically_CVPR_2017_paper.pdf)

## 2018
- Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification ICIAR 2018 [[paper]](https://arxiv.org/abs/1802.00931)
- Cancer Metastasis Detection With Neural Conditional Random Field MIDL 2018 [[paper]](https://arxiv.org/abs/1806.07064)

----------------------------------------------------------------------------------------------------------------------------------------
# Colonoscopy
## 2016
- Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning TMI 2016 [[papr]](http://ieeexplore.ieee.org/document/7426826/)

## 2018
- Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional Network [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-73603-7_32)

----------------------------------------------------------------------------------------------------------------------------------------
# OCT
## 2017
- Cystoid Macular Edema Segmentation of Optical Coherence Tomography Images Using Fully Convolutional Neural Networks and Fully Connected CRFs 2017 [[paper]](https://arxiv.org/abs/1709.05324)

----------------------------------------------------------------------------------------------------------------------------------------
# Dermoscopy
## 2016
- Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks [[paper]](http://ieeexplore.ieee.org/document/7792699/)
- Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector [[paper]](https://ieeexplore.ieee.org/document/7950524/)
- Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification [[paper]](https://ieeexplore.ieee.org/document/7493447/)

## 2017
- Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance [[paper]](http://ieeexplore.ieee.org/document/7903636/)
"Jaccard distance on one hand, is similar to the known Dice overlap coefficient (also a novel loss function in V-Net), on the other hand, in the above paper, is a novel loss function suitable for binary class segmentation task. obviously, Jaccard distance is similar to IoU (intersection over union), a strict metric in object/semantic segmentation in computer vision."
- Investigating deep side layers for skin lesion segmentation [[paper]](https://ieeexplore.ieee.org/document/7950514/)
- Skin Lesion Segmentation via Deep RefineNet [[paper]](https://link.springer.com/chapter/10.1007%2F978-3-319-67558-9_35)
- Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks [[paper]](https://ieeexplore.ieee.org/document/8239798/)
- Segmentation of dermoscopy images based on fully convolutional neural network [[paper]](https://ieeexplore.ieee.org/document/8296578/)
- Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks [[paper]](https://arxiv.org/abs/1711.10449)
"Multi-class (classification and segmentation)"
- Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks [[paper]](https://ieeexplore.ieee.org/document/8239798/)
- Dermoscopic Image Segmentation via Multi-Stage Fully Convolutional Networks [[paper]](http://ieeexplore.ieee.org/document/7942129/)
- Skin melanoma segmentation using recurrent and convolutional neural networks [[paper]](https://ieeexplore.ieee.org/document/7950522/)
- Skin Lesion Classification Using Hybrid Deep Neural Networks 2017 [[paper]](https://arxiv.org/abs/1702.08434)

## 2018
- DermoNet Densely Linked Convolutional Neural Network for Efficient Skin Lesion Segmentation [[paper]](https://openreview.net/forum?id=B167qcojM)
- Techniques and Algorithms for Computer Aided Diagnosis of Pigmented Skin Lesions A Review [[paper]](https://www.sciencedirect.com/science/article/pii/S1746809417301428)
- MelanoGANs High Resolution Skin Lesion Synthesis with GANs [[paper]](https://arxiv.org/abs/1804.04338)
- SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks MICCAI 2018 [[paper]](https://arxiv.org/abs/1805.10241)
- Skin Lesion Classification with Ensemble of Squeeze-and-excitation Networks and Semi-supervised Learning 2018 [[paper]](https://arxiv.org/abs/1809.02568)

----------------------------------------------------------------------------------------------------------------------------------------
# Endoscopy
## 2018
- Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks 2018 [[paper]](https://ieeexplore.ieee.org/document/8259318/) [[code]](https://github.com/surgical-vision/EndoVisPoseAnnotation)

前进明亮/Medical-Image-Analysis-MIPAV

简介

发行版

贡献者

近期动态

前进明亮/Medical-Image-Analysis-MIPAV .gitee-modal { width: 500px !important; }

简介

发行版

贡献者

近期动态

搜索帮助

　前进明亮/Medical-Image-Analysis-MIPAV