Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [paper]
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network [paper]
Sharpness-aware Low Dose CT Denoising Using Conditional Generative Adversarial Network [paper]
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT [paper]
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image [paepr]
A Self-aware Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation [paper]
DeepLesion Automated Deep Mining Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations [paper]
Unsupervised End-to-end Learning for Deformable Medical Image Registration [paper]
DeepLung 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification [paper]
DeepLung Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification [paper]
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans [paper]
Medical Image Synthesis with Context-aware Generative Adversarial Networks [paper]
SegAN Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [paper]
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images [paper]
Deep MR to CT Synthesis using Unpaired Data [paper]
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT [paper]
3D Fully Convolutional Networks for Subcortical Segmentation in MRI A Large-scale Study [paper] [code]
2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation [paper]
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI [paper]
Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) For Brain Matter Segmentation [paper]
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks [paper]
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks [paper]
Stacked Deep Polynomial Network Based Representation Learning for Tumor Classification with Small Ultrasound Image Dataset [paper]
Convolutional Neural Networks for Medical Image Analysis Full Training or Fine Tuning [paepr]
Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [paper]
Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning [paper]
Anatomically Constrained Neural Networks (ACNN) Application to Cardiac Image Enhancement and Segmentation [paper]
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks [paper]
Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks [paper]
Virtual PET Images from CT Data Using Deep Convolutional Networks Initial Results [paper]
Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [paper] [Keras+TF code]
Stain Normalization Using Sparse AutoEncoders (StaNoSA) Application to Digital Pathology [paper]
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images [paper]
Adversarial Image Alignment and Interpolation [paper]
CNN Cascades for Segmenting Whole Slide Images of the Kidney [paper]
Learning to Segment Breast Biopsy Whole Slide Images [paper]
SFCN-OPI Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction [paper]
Real-Time Polyps Segmentation for Colonoscopy Video Frames Using Compressed Fully Convolutional Network [paper]
Cystoid Macular Edema Segmentation of Optical Coherence Tomography Images Using Fully Convolutional Neural Networks and Fully Connected CRFs 2017 [paper]
Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance [paper]
"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."
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