周读论文系列笔记(2)-reivew-A survey on Deep Learning in Medical Image Analysis

刚接触这个领域…不怎么会写…有些翻译错和理解错的地方请大佬们多多指教~

这篇论文分为四个部分：
Deep learning methods
Deep learning uses in medical imaging
Application areas
Challenges and outlook
第1部分在这里就不写了…写剩下的部分

原文链接：https://www.sciencedirect.com/science/article/pii/S1361841517301135

1.Deep learning methods

2.Deep learning uses in medical imaging

2.1 Classification 分类

2.1.1 Image/exam classification （图像/exam 分类）

Image or exam classification was one of the first areas in which deep learning made a major contribution to medical image analysis.

In exam classification one typically has one or multiple images (an exam) as input with a single diagnostic variable as output (e.g., disease present or not).

Dataset sizes are small -> transfer learning

Two transfer learning strategies were identified:
(1) using a pre-trained network as a feature extractor.
(2) fine-tuning a pre-trained network on medical data.
The former strategy has the extra benefit of not requiring one to train a deep network at all, allowing the extracted features to be easily plugged in to existing image analysis pipelines. Both strategies are popular and have been widely applied. Few authors perform a investigation in which strategy gives the best result.

Methods:
(1)Initially focus on unsupervised pre-training and network architectures like SAEs(Sparse Autoencoder稀疏自编码器) and RBMs(Restricted Boltzmann Machine 受限玻尔兹曼机).
(2)CNN (in 2015, 2016, 2017)
The application areas ranging from brain MRI to retinal imaging(视网膜成像) and digital pathology(数字病理学) to lung computed tomography(肺部计算机断层扫描).
(3)In the more recent papers using CNNs authors also often train their own network architectures from scratch instead of using pre-trained networks.
(4)Three papers used an architecture leveraging the unique attributes of medical data.(3D…)

Summary: in exam classification CNNs are the current standard techniques. Especially CNNs pre- trained on natural images have shown surprisingly strong results, challenging the accuracy of human ex- perts in some tasks. Last, authors have shown that CNNs can be adapted to leverage intrinsic structure of medical images.

2.1.2 Object or lesion classification （object或病变分类）

Object classification usually focuses on the classification of a small (previously identified) part of the medical image into two or more classes (e.g. nodule classification in chest CT).

For many of these tasks both local information on lesion appearance and global contextual information on lesion location are required for accurate classification.
This combination is typically not possible in generic deep learning architectures.

Methods:
(1)Almost all recent papers prefer the use of end-to-end trained CNNs.
Several authors have used multi-stream architectures to resolve this in a multi-scale fashion.
three CNNs(each of which takes a nodule patch), a combination of CNNs and RNNs(for grading nuclear cataracts对核白内障分级) , 3D CNN(high-grade gliomas高级别胶质瘤)

(2)In some cases other architectures and approaches are used, such as RBMs (Restricted Boltzmann Machine 受限玻尔兹曼机) SAEs (Sparse Autoencoder稀疏自编码器) and convolutional sparse auto-encoders (CSAE) (卷积稀疏自编码器). The major difference between CSAE and a classic CNN is the usage of unsupervised pre-training with sparse auto-encoders.

(3)An interesting approach, especially in cases where object annotation to generate training data is expensive, is the integration of multiple instance learning (MIL多实例学习) and deep learning.

Summary:
object classification sees less use of pre-trained networks compared to exam classifications, mostly due to the need for incorporation of contextual or 3D information. Several authors have found innovative solutions to add this information to deep networks with good results, and as such we expect deep learning to become even more prominent for this task in the near future.

2.2 Detection 检测

2.2.1 Organ, region and landmark localization （器官 定位）

Anatomical object localization (in space or time), such as organs or landmarks, has been an important pre-processing step in segmentation tasks or in the clinical workflow for therapy planning and intervention.
Localization in medical imaging often requires parsing of 3D volumes.

Methods:
Space:
(1)To solve 3D data parsing with deep learning algorithms, several approaches have been proposed that treat the 3D space as a composition of 2D orthogonal planes.
(2)Other authors try to modify the network learning pro- cess to directly predict locations. (Due to its increased complexity, only a few methods addressed the direct localization of landmarks and regions in the 3D image space.)
Time:
(1)CNNs have also been used for the localization of scan planes or key frames in temporal data.
(2)RNN, particularly LSTM-RNNS, have also been used to exploit the temporal information contained in medical videos, another type of high dimensional data.
(3)Combine an LSTM-RNN with a CNN

Summary:
Localization through 2D image classification with CNNs seems to be the most popular strategy overall to identify organs, regions and landmarks, with good results.
However, several recent papers expand on this concept by modifying the learning process such that accurate localization is directly emphasized, with promising results.
We expect such strategies to be explored further as they show that deep learning techniques can be adapted to a wide range of localization tasks (e.g. multiple landmarks).
RNNs have shown promise in localization in the temporal domain, and multi-dimensional RNNs could play a role in spatial localization as well.

2.2.2 Object or lesion detection （obje