今日CS.CV计算机视觉论文速览
Wed, 13 Mar 2019
Totally 25 papers
?自动医学图像分析，主要就x光乳腺癌检测，胸片CT肺结合检测，脑部颈部病变检测等方面展开研究，并阐述了如何生成数据、利用弱监督标签、结合boosting方法等。（欧文分校博士论文）
1 Introduction
2 Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
3 Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
4 DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
5 DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
6 AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
7 Conclusion
软件著作：SOFTWARE
AnatomyNet
https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation
Deep learning for fast and fully automated whole-volume segmentation of head and neck
anatomy.
DeepEM
https://github.com/wentaozhu/DeepEM-for-Weakly-Supervised-Detection
Deep 3D ConvNets with EM for weakly supervised pulmonary nodule detection.
DeepLung
xiv
https://github.com/wentaozhu/DeepLung
Deep 3d dual path nets for automated pulmonary nodule detection and classification.
Adversarial DSN
https://github.com/wentaozhu/adversarial-deep-structural-networks
Adversarial Deep Structural Networks for Mammographic Mass Segmentation.
Deep MIL
https://github.com/wentaozhu/deep-mil-for-whole-mammogram-classification
Deep multi-instance networks with sparse label assignment for whole mammogram classifi-
cation.
Regularized Deep LSTM
http://www.escience.cn/system/file?fileId=87579
Co-Occurrence feature learning for skeleton based action recognition using regularized deep
LSTM networks.
?深度学习架构的理解相似性和差异性，主要集中于比较模型间和的相似性，研究发现核相似性达到99.9%的模型表现却不尽相同，而没用太大相似性的架构却有着相同的表现！
20个网络的相关性分析，表格中的数据为模型在相同视觉智能和参数相关性上的差异：
12种resnet的相关性分析，
?从RGB图像生成复杂的形态学网格,主要集中与复杂形态的重建，提出了基于骨架媒介的和此方法，利用骨架保持形态并减小计算复杂度，并逐阶段纠正体积和重建和网格的精调。（from 华南理工）
骨架体K合成的流程以及mesh优化的结果：
dataset：ShapeNet-Skeleton dataset
relate: 3D Shape net:http://3dshapenets.cs.princeton.edu/
3D Shape Synthesis and Recognition
?GOGGLES,基于数据编程,多源数据的弱监督（data programming，label function）和相似性（affinity）编码实现自动数据生成。
code: https://github.com/chu-data-lab/GOGGLES/
数据集：http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
?CaP，Cascaded Projection, 端到端的神经网络压缩和加速工具，基于数据驱动的方法实现，在保持高精度和高通量的前提下极大减小内存消耗。通过低秩投影的方法将输入输出连续的滤波器投影到统一的低维空间中来实现压缩，并通过最小化分类损失和特征间的差距来优化投影过程，并通过bp和sgd在几何约束下得到代理矩阵来进行优化，解决了精度、大小和速度的问题。（from rit Rochester Institute of Technology）
压缩优化过程：
?非监督的视频显著性物体检测，主要利用了流补全的技术。首先利用光流检测备选区域得到光流边界，随后通过光流和补全流之间的差异得到残差流，并以此为线索得到运动显著性掩膜。只依赖于运动信息让这种方法具有灵活性和普适性。（from Inria, Centre Rennes）
相关方法和指标：
相关数据集和基准：https://davischallenge.org/davis2016/code.html
?快速深度图生成,提出了一种更高效的网络架构用于从双目视觉生成视差图。研究人员利用了半分辨率的输入，减小了网络计算量，并且使用了低维（视差）特征向量来实现了图像间的配准。(from Swarthmore College,华盛顿大学)
code:https://projects.ayanc.org/fdscs/
?Parallel Medical Imaging,(from 中科院自动化所)
数据合成方法值得注意：
!!!注：自动生成ct图像
?自适应迁移学习综述，（from 重庆大学）
几种自适应结构
?手部骨骼分割，（from 新加坡国立）
dataset: 2017 Pediatric Bone Age Prediction Challenge [23]
?利用深度学习生成超像素，（from KU Leuven）
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