「MICCAI 2017」Reading Notes

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    此文记录MICCAI 2017会议论文阅读笔记，主要通过论文题目和摘要及其个人研究兴趣，摘取一些，他日跟进。

    会议论文一共有三个部分，共享在此：here,，而内容分为：（加粗为我感兴趣的部分）

    1、Atlas and Surface-Based Techniques

    2、Shape and Patch-Based Techniques

    3、Registration Techniques

    4、Functional Imaging, Connectivity, and Brain Parcellation

    5、Diffusion Magnetic Resonance Imaging (dMRI) and Tensor/Fiber Processing

    6、Image Segmentation and Modelling

    7、Optical Imaging

    8、Airway and Vessel Analysis

    9、Motion and Cardiac Analysis

    10、Tumor Processing

    11、Planning and Simulation for Medical Interventions

    12、Interventional Imaging and Navigation

    13、Medical Image Computing

    14、Feature Extraction and Classification Techniques

    15、Machine Learning in Medical Image Computing

Shape and Patch-Based Techniques

Integrating Statistical Prior Knowledge into Convolutional Neural Networks （P1 161）

Learning and Incorporating Shape Models for Semantic Segmentation （P1 203）

Image Segmentation and Modelling

Towards Automatic Semantic Segmentation in Volumetric Ultrasound*（P1 711）

Optical Imaging

Histological Detection of High-Risk Benign Breast Lesions from Whole Slide Images （P2 144）

Motion and Cardiac Analysis

Detection and Characterization of the Fetal Heartbeat in Free-hand Ultrasound Sweeps with Weakly-supervised Two-streams Convolutional Networks （P2 305）

Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video （P2 341）

Interventional Imaging and Navigation

Deep Learning for Sensorless 3D Freehand Ultrasound Imaging （P2 627）
Precise Ultrasound Bone Registration with Learning-Based Segmentation and Speed of Sound Calibration （P2 682）

Machine Learning in Medical Image Computing

Robust Multi-modal MR Image Synthesis （P3 347）

Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks （P3 356）

Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis* （P3 382）

Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation （P3 399）

Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images （P3 408）

Medical Image Synthesis with Context-Aware Generative Adversarial Networks （P3 417）

SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging （P3 435）

X-Ray In-Depth Decomposition: Revealing the Latent Structures （P3 444）

Deep Convolutional Encoder-Decoders for Prostate Cancer Detection and Classification （P3 489）

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network （P3 507）

Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning* （P3 630）