php网站开发示例,做网站必须先买域名吗,王烨在地府是什么身份,低代码建站平台论文网址#xff1a;Frontiers | A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis (frontiersin.org)
英文是纯手打的#xff01;论文原文的summarizing and paraphrasing。可能会出现难以避免的拼写错误和语法错误#xff0c;若有发现欢迎评论…
论文网址Frontiers | A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis (frontiersin.org)
英文是纯手打的论文原文的summarizing and paraphrasing。可能会出现难以避免的拼写错误和语法错误若有发现欢迎评论指正文章偏向于笔记谨慎食用 目录
1. 省流版
1.1. 心得
1.2. 论文总结图
2. 论文逐段精读
2.1. Abstract
2.2. Introduction
2.3. Deep Learning
2.3.1. Feed-Forward Neural Networks
2.3.2. Stacked Auto-Encoders
2.3.3. Deep Belief Networks
2.3.4. Deep Boltzmann Machine
2.3.5. Generative Adversarial Networks
2.3.6. Convolutional Neural Networks
2.3.7. Graph Convolutional Networks
2.3.8. Recurrent Neural Networks
2.3.9. Open Source Deep Learning Library
2.4. Applications in Brain Disorder Analysis With Medical Images
2.4.1. Deep Learning for Alzheimers Disease Analysis
2.4.2. Deep Learning for Parkinsons Disease Analysis
2.4.3. Deep Learning for Austism Spectrum Disorder Analysis
2.4.4. Deep Learning for Schizophrenia Analysis
2.5. Discussion and Future Direction
2.6. Conclusion
3. Reference List 1. 省流版
1.1. 心得
1上来直接就开模型介绍文心吃这些东西吃多了吧
2我觉得不该把疾病分开诶现在很多模型不都为了泛化而用在几个疾病数据集上吗
3⭐在可解释性和数据集上给出解决办法是值得认可的
4哥们儿正文和discussion是一个人写的吗discussion写这么好怎么正文跟 1.2. 论文总结图 2. 论文逐段精读
2.1. Abstract ①Structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET) can all be used in neuroimage analysis ②Disease included: Alzheimers disease, Parkinsons disease, Autism spectrum disorder, and Schizophrenia 2.2. Introduction ①Introducing medical imaging ②Therefore, the feature selection step is extremely important for complex medical image processing. Although sparse learning and dictionary learning have been used to extract features, their shallow architectures still limit their representation ability. ③The development of hardware promotes the improvement of deep learning in medical image analysis ④Categories of medical imaging analysis: classification, detection/localization, registration, and segmentation ⑤This survey mainly centers on brain disease cardiac adj.心脏的;心脏病的 n.心脏病患者;强心剂;健胃剂 2.3. Deep Learning
2.3.1. Feed-Forward Neural Networks ①The function of FFNN: where the is the input vector, is the output;
superscript denotes layer index, is the number of hidden units; and are bias terms of input layer hidden layer respectively; and denote non-linear activation function; represents parameter set ②Sketch map of (A) single and (B) multi layer neural networks: 2.3.2. Stacked Auto-Encoders ①Auto-encoder (AE), namely so called auto-associator, possesses the ability of encoding and decoding ②AE can be stacked as stacked auto-encoders (SAE) with better performance ③Sketch map of SAE: where the blue and red dot boxes are encoder and decoder respectively ④To avoid being trapped in local optimal solution, SAE applies layer-wise pretraining methods 2.3.3. Deep Belief Networks ①By stacking multiple restricted Bolztman machines (RBMs), the Deep Belief Network (DBN) is constructed ②The joint distribution of DBN: where denotes visible units and denotes hidden layers ③Sketch map of (A) DBN and (B) DBM: where the double-headed arrow denotes undirected connection and the single-headed arrow denotes directed connection 2.3.4. Deep Boltzmann Machine ①Futher stacking RBMs can get Deep Boltzmann Machine (DBM): 2.3.5. Generative Adversarial Networks ①Simultaneously including generator and discriminator , Generative Adversarial Networks (GANs) achieves the task of training models with a small number of labeled samples: ②The framework of GAN: 2.3.6. Convolutional Neural Networks ①The framework of convolutional neural network (CNN): 2.3.7. Graph Convolutional Networks ①The framework of Graph Convolutional Networks (GCN): which includes spectral-based and spatial-based methods 2.3.8. Recurrent Neural Networks ①As the extension of FFNN, recurrent neural network (RNN) ia able to learn features and long-term dependencies from sequential and time-series data ②Framework of (A) long-short-term memory (LSTM) and (B) Gated Recurrent Unit (GRU): 2.3.9. Open Source Deep Learning Library ①Some toolkits of deep learning: 2.4. Applications in Brain Disorder Analysis With Medical Images
2.4.1. Deep Learning for Alzheimers Disease Analysis ①Introducing the Alzheimers Disease Neuroimaging Initiative (ADNI) and the classification method of patients ②Enumerating DGM based and CNN based methods, 2D CNN and 3D CNN ③Articles which applying DL in AD detection: ④Classification performance of these articles: ⑤Articles that applying DL to predict MCI: ⑥Prediction performance of artivles above: 2.4.2. Deep Learning for Parkinsons Disease Analysis ①Dataset example: Parkinsons Progression Markers Initiative (PPMI) ②Exampling some DL works on PD diagnosis ③Articles which applying DL in PD detection: 2.4.3. Deep Learning for Austism Spectrum Disorder Analysis ①Dataset: ABIDE I/II ②Particularizing AE/CNN/RNN based methods ③Articles that applying DL to ASD diagnosis: 2.4.4. Deep Learning for Schizophrenia Analysis ①There is no widely used SZ neuroimaging dataset available currently ②Dataset from challenge: The MLSP2014 (Machine Learning for Signal Processing) SZ classification challenge, with 75 NC and 69 SZ ③Articles which applying DL in SZ detection: 2.5. Discussion and Future Direction ①Hyper-parameters of DL:
model optimization parametersthe optimization method, learning rate, and batch sizes, etc.network structure parametersnumber of hidden layers and units, dropout rate, activation function, etc. ②Optimization of hyper-parameters:
manualgrid search and random searchautomatic Bayesian Optimization ③Deep learning still faces the challenges of weak interpretability, limited multi-modality and limited data in imaging studies 2.6. Conclusion Medicine and computers will inevitably merge 3. Reference List
Zhang, L. et al. (2020) A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis, Front Neurosci. doi: 10.3389/fnins.2020.00779
