赞
踩
据我们所知,这是第一份关于医学应用的深度学习论文清单。一般来说,有很多深度学习论文或计算机视觉的列表,例如Awesome Deep Learning Papers。在此列表中,我尝试根据他们的深度学习技巧和学习方法对论文进行分类。我相信这份清单可能是一个很好的起点对于深度学习医学应用研究人员而言。
期刊会议列表
深度学习
医学名词
计算机技术 | 医学技术 | 目标区域 | 标题 | 数据库 | J/C | 年份 |
---|---|---|---|---|---|---|
NN | H&E | 无 | Deep learning of feature representation with multiple instance learning for medical image analysis [pdf] | ICASSP | 2014 | |
M-CNN | H&E | 乳腺 | AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images [pdf] | AMIDA | IEEE-TMI | 2016 |
FCN | H&E | 无 | Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation pdf | MICCAI | 2017 |
计算机技术 | 医学技术 | 目标区域 | 标题 | 数据库 | J/C | 年份 |
---|---|---|---|---|---|---|
M-CNN | CT | 肺 | Multi-scale Convolutional Neural Networks for Lung Nodule Classification [pdf] | LIDC-IDRI | IPMI | 2015 |
3D-CNN | MRI | 大脑 | Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks [pdf] | ADNI | arXiv | 2015 |
CNN+RNN | RGB | 眼睛 | Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning [pdf] | IEEE-TBME | 2015 | |
CNN | X-ray | 膝盖 | Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks [pdf] | O.E.1 | arXiv | 2016 |
CNN | H&E | 甲状腺 | A Deep Semantic Mobile Application for Thyroid Cytopathology [pdf] | SPIE | 2016 | |
3D-CNN, 3D-CAE | MRI | 大脑 | Alzheimer’s Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network [pdf] | ADNI | arXiv | 2016 |
M-CNN | RGB | 皮肤 | Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers [pdf] | Dermofit | MLMI | 2016 |
CNN | RGB | 皮肤,眼睛 | Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes [pdf] | EDRA, DRD | arXiv | 2016 |
M-CNN | CT | 肺 | Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks [pdf] | LIDC-IDRI, ANODE09, DLCST | IEEE-TMI | 2016 |
3D-CNN | CT | 肺 | DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification [pdf] | LIDC-IDRI, LUNA16 | IEEE-WACV | 2018 |
3D-CNN | MRI | 大脑 | 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients [pdf] | MICCAI | 2016 | |
SAE | US, CT | 乳腺,大脑 | Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans [pdf] | LIDC-IDRI | Nature | 2016 |
CAE | MG | 乳腺 | Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring [pdf] | IEEE-TMI | 2016 | |
MIL-CNN | MG | 乳腺 | Deep multi-instance networks with sparse label assignment for whole mammogram classification [pdf] | INbreast | MICCAI | 2017 |
GCN | MRI | 大脑 | Spectral Graph Convolutions for Population-based Disease Prediction [pdf] | ADNI, ABIDE | arXiv | 2017 |
CNN | RGB | 皮肤 | Dermatologist-level classification of skin cancer with deep neural networks | Nature | 2017 | |
FCN + CNN | MRI | 肝,肝癌 | SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks [pdf] | ISBI | 2017 |
计算机技术 | 医学技术 | 目标区域 | 标题 | 数据库 | J/C | 年份 |
---|---|---|---|---|---|---|
MLP | CT | 头颈 | 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data [pdf] | MICCAI | 2015 | |
CNN | US | 胎儿 | Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks [pdf] | IEEE-JBHI | 2015 | |
2.5D-CNN | MRI | 股骨 | Automated anatomical landmark detection ondistal femur surface using convolutional neural network [pdf] | OAI | ISBI | 2015 |
LSTM | US | 胎儿 | Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks [pdf] | MICCAI | 2015 | |
CNN | X-ray, MRI | 头部 | Regressing Heatmaps for Multiple Landmark Localization using CNNs [pdf] | DHADS | MICCAI | 2016 |
CNN | MRI, US, CT | - | An artificial agent for anatomical landmark detection in medical images [pdf] | SATCOM | MICCAI | 2016 |
FCN | US | 胎儿 | Real-time Standard Scan Plane Detection and Localisation in Fetal Ultrasound using Fully Convolutional Neural Networks [pdf] | MICCAI | 2016 | |
CNN+LSTM | MRI | 心脏 | Recognizing end-diastole and end-systole frames via deep temporal regression network [pdf] | MICCAI | 2016 | |
M-CNN | MRI | 心脏 | Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation Neural Networks [pdf] | IEEE-TMI | 2016 | |
CNN | PET/CT | 心脏 | Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique Neural Networks [pdf] | MP | 2016 | |
3D-CNN | MRI | 大脑 | Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks [pdf] | IEEE-TMI | 2016 | |
CNN | X-ray, MG | - | Self-Transfer Learning for Fully Weakly Supervised Lesion Localization [pdf] | NIH,China, DDSM,MIAS | MICCAI | 2016 |
CNN | RGB | 眼睛 | Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images [pdf] | DRD, MESSIDOR | MICCAI | 2016 |
GAN | - | - | Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery | IPMI | 2017 | |
FCN | X-ray | 心脏 | CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes | MIAR | 2016 | |
3D-CNN | CT | 肺 | DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification [pdf] | LIDC-IDRI, LUNA16 | IEEE-WACV | 2018 |
3D-CNN | CT | 肺 | DeepEM: Deep 3D ConvNets with EM for weakly supervised pulmonary nodule detection [pdf] | LIDC-IDRI, LUNA16 | MICCAI | 2018 |
计算机技术 | 医学技术 | 目标区域 | 标题 | 数据库 | J/C | 年份 |
---|---|---|---|---|---|---|
U-Net | - | - | U-net: Convolutional networks for biomedical image segmentation | MICCAI | 2015 | |
FCN | MRI | 头颈 | Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation [pdf] | arXiv | 2016 | |
FCN | CT | 肝,肝癌 | Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields [pdf] | MICCAI | 2016 | |
3D-CNN | MRI | 脊柱 | Model-Based Segmentation of Vertebral Bodies from MR Images with 3D CNNs | MICCAI | 2016 | |
FCN | CT | 肝,肝癌 | Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks [pdf] | arXiv | 2017 | |
FCN | MRI | 肝,肝癌 | SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks [pdf] | ISBI | 2017 | |
3D-CNN | Diffusion MRI | 大脑 | q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI [pdf] (Section II.B.2) | IEEE-TMI | 2016 | |
GAN | MG | 心肌 | Adversarial Deep Structured Nets for Mass Segmentation from Mammograms [pdf] | INbreast, DDSM-BCRP | ISBI | 2018 |
3D-CNN | CT | 肝脏 | 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes pdf | MICCAI | 2017 | |
3D-CNN | MRI | 大脑 | Unsupervised domain adaptation in brain lesion segmentation with adversarial networks pdf | IPMI | 2017 | |
FCN | FUNDUS | 眼睛/视网膜 | A Fully Convolutional Neural Network based Structured Prediction Approach Towards the Retinal Vessel Segmentation pdf | ISBI | 2017 |
计算机技术 | 医学技术 | 目标区域 | 标题 | 数据库 | J/C | 年份 |
---|---|---|---|---|---|---|
3D-CNN | CT | 脊柱 | An Artificial Agent for Robust Image Registration [pdf] | 2016 |
计算机技术 | 医学技术 | 目标区域 | 标题 | 数据库 | J/C | 年份 |
---|---|---|---|---|---|---|
2.5D-CNN | MRI | Automated anatomical landmark detection ondistal femur surface using convolutional neural network [pdf] | OAI | ISBI | 2015 | |
3D-CNN | Diffusion MRI | 大脑 | q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI [pdf] (Section II.B.1) | [HCP] and other | IEEE-TMI | 2016 |
Copyright © 2003-2013 www.wpsshop.cn 版权所有,并保留所有权利。