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https://github.com/Engineering-Course/Awesome-Medical-Research

Awesome list for deep learning on medical research
https://github.com/Engineering-Course/Awesome-Medical-Research
List: Awesome-Medical-Research
awesome-list deep-learning medical-application
Last synced: about 2 months ago
JSON representation
Awesome list for deep learning on medical research
Host: GitHub
URL: https://github.com/Engineering-Course/Awesome-Medical-Research
Owner: Engineering-Course
Created: 2018-08-22T12:34:54.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2020-10-01T11:23:20.000Z (almost 4 years ago)
Last Synced: 2024-05-23T07:41:38.488Z (4 months ago)
Topics: awesome-list, deep-learning, medical-application
Size: 200 KB
Stars: 48
Watchers: 6
Forks: 8
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome-interesting-topics-in-nlp - DL
ultimate-awesome - Awesome-Medical-Research - Awesome list for deep learning on medical research. (Other Lists / PowerShell Lists)
README

        # Awesome-Medical-Research [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/Engineering-Course/Awesome-Medical-Research)

Awesome list for deep learning on medical research

## Contents

- [Dialog Systems](#dialog-systems)

- [Question Answering Systems](#question-answering-systems)

- [Medical Imaging Reports](#medical-imaging-reports)

- [Lesion Detection](#lesion-detection)

- [Object Detection](#object-detection)

- [Graph Reasoning](#graph-reasoing)

- [Natural Language Processing](#natural-language-processing)

- [Paper Connect](#paper-connect)

- [Challenge](#challenge)

- [Blog](#blog)

- [News](#news)

- [Slides](#slides)

- [Website](#website)

- [Online Resources](#online-resources)

- [Online Courses](#online-courses)

- [Datasets](#datasets)

- [Related Awesome Lists](#related-awesome-lists)

## Dialog Systems

* Task-oriented Dialogue System for Automatic Diagnosis [`ACL 2018`](http://www.sdspeople.fudan.edu.cn/zywei/paper/liu-acl2018.pdf) [`code`](https://github.com/LiuQL2/MedicalChatbot)

 

* Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems [`ACL 2018`](http://aclweb.org/anthology/P18-1136) [`code`](https://github.com/HLTCHKUST/Mem2Seq)

  

* Knowledge Diffusion for Neural Dialogue Generation [`ACL 2018`](http://aclweb.org/anthology/P18-1138) [`code`](https://github.com/jiweil/Neural-Dialogue-Generation) 

  

* Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings [`ACL 2017`](https://arxiv.org/pdf/1704.07130.pdf) [`code`](https://github.com/stanfordnlp/cocoa)

 

* KNOWLEDGE-POWERED CONVERSATIONAL AGENTS [`ICLR 2019`](https://openreview.net/pdf?id=r1l73iRqKm) [`related code`](https://github.com/GetStoryline/awesome-bots) [`related code`](https://github.com/ricsinaruto/Seq2seqChatbots)

 

* GLOBAL-TO-LOCAL MEMORY POINTER NETWORKS FOR TASK-ORIENTED DIALOGUE [`ICLR 2019`](https://openreview.net/pdf?id=ryxnHhRqFm)

 

* Decoupling Strategy and Generation in Negotiation Dialogues [`arxiv`](https://arxiv.org/pdf/1808.09637.pdf)

 

* BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [`arxiv`](https://arxiv.org/abs/1810.04805) [`code`](https://github.com/codertimo/BERT-pytorch)

 

* DIFFERENTIABLE EXPECTED BLEU FOR TEXT GENERATION [`ICLR 2019`](https://openreview.net/pdf?id=S1x2aiRqFX)

 

* Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access [`arxiv`](https://arxiv.org/pdf/1609.00777.pdf) [`code`](https://github.com/MiuLab/KB-InfoBot)

 

## Question Answering Systems

* Denoising Distantly Supervised Open-Domain Question Answering [`ACL 2018`](http://aclweb.org/anthology/P18-1161) [`code`](https://github.com/thunlp/OpenQA)

* Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders [`arxiv`](https://arxiv.org/abs/1805.04843) [`code`](https://github.com/victorywys/Learning2Ask_TypedDecoder)

* Interpretation of Natural Language Rules in Conversational Machine Reading [`arxiv`](https://arxiv.org/pdf/1809.01494.pdf)

  

* Improving Question Answering by Commonsense-Based Pre-Training [`arxiv`](https://arxiv.org/abs/1809.03568) [`related`](https://github.com/sebastianruder/NLP-progress/blob/master/question_answering.md)

 

* A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC [`arxiv`](https://arxiv.org/abs/1809.10735)

 

* Learning Visual Knowledge Memory Networks for Visual Question Answering [`CVPR 2018`](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/4255.pdf) [`related code`](https://github.com/JamesChuanggg/awesome-vqa)

 

 

## Medical Imaging Reports

* On the Automatic Generation of Medical Imaging Reports [`ACL 2018`](http://aclweb.org/anthology/P18-1240) [`code`](https://github.com/ZexinYan/Medical-Report-Generation)

  

  

## Lesion Detection

* Deep Lesion Graphs in the Wild [`CVPR 2018`](https://arxiv.org/pdf/1711.10535.pdf) [`code`](https://github.com/rsummers11/CADLab/tree/master/lesion_detector_3DCE)

 

* DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning [`JMI 2018`](https://arxiv.org/pdf/1710.01766.pdf) [`code`](https://github.com/rsummers11/CADLab/tree/master/lesion_detector_3DCE)

 

* 3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection [`MICCAI 2018`](https://arxiv.org/pdf/1806.09648.pdf) [`code`](https://github.com/rsummers11/CADLab/tree/master/lesion_detector_3DCE)

## Object Dection

* CornerNet: Detecting Objects as Paired Keypoints [`arxiv`](https://arxiv.org/pdf/1808.01244.pdf) [`code`](https://github.com/princeton-vl/CornerNet)

* Learning Deep Representations for Visual Recognition [`CVPR 2018`](http://kaiminghe.com/cvpr18tutorial/cvpr2018_tutorial_kaiminghe.pdf) [`related code`](https://github.com/reedscot/cvpr2016) [`video`](https://www.youtube.com/watch?v=jHv37mKAhV4)

* Higher-order Graph Convolutional Networks [`arxiv`](https://arxiv.org/abs/1809.07697) [`related code`](https://github.com/tkipf/gcn) [`related code`](https://github.com/tkipf/keras-gcn) [`related code`](https://github.com/tkipf/pygcn)

 

* Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection [`arxiv`](https://arxiv.org/abs/1809.08545) [`code`](https://github.com/yihui-he/softer-NMS)

 

* Searching for Efficient Multi-Scale Architectures for Dense Image Prediction [`arxiv`](https://arxiv.org/pdf/1809.04184.pdf)

 [`related code`](https://github.com/tensorflow/models/tree/master/research/deeplab) [`related`](https://github.com/bonlime/keras-deeplab-v3-plus)

  

## Graph Reasoning

* GO FOR A WALK AND ARRIVE AT THE ANSWER [`ICLR 2018`](https://arxiv.org/pdf/1711.05851.pdf) [`code`](https://github.com/shehzaadzd/MINERVA)

 

* GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations [`arxiv`](https://arxiv.org/pdf/1806.05662.pdf)

 

* Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks [`arxiv`](https://arxiv.org/pdf/1809.02040.pdf) [`related link`](https://github.com/shagunsodhani/Graph-Reading-Group)

* GRAPH CONVOLUTIONAL NETWORK WITH SEQUENTIAL ATTENTION FOR GOAL-ORIENTED DIALOGUE SYSTEMS [`ICLR 2019`](https://openreview.net/pdf?id=Skz-3j05tm)

  

* Neural Natural Language Inference Models Enhanced with External Knowledge [`arxiv`](https://arxiv.org/pdf/1711.04289.pdf) [`code`](https://github.com/feifengwhu/NLP_External_knowledge)

* Enhanced LSTM for Natural Language Inference [`arxiv`](https://arxiv.org/abs/1609.06038) [`code`](https://github.com/lukecq1231/nli)

* Leveraging Knowledge Bases in LSTMs for Improving Machine Reading [`ACL 2017`](https://www.cs.cmu.edu/~bishan/papers/kblstm_acl2017.pdf)

## Natural Language Processing

* Universal Transformers [`arxiv`](https://arxiv.org/pdf/1807.03819.pdf) [`code`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/research/universal_transformer.py)

 

* NEWSROOM: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies [`NAACL 2018`](https://yoavartzi.com/pub/gna-naacl.2018.pdf) [`code`](https://github.com/clic-lab/newsroom)

 

* Contextual and Structural Language [`semanticscholar`](https://pdfs.semanticscholar.org/e86b/b512325f4e6b39446337999b8c9933867649.pdf)

 

* Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks [`arxiv`](https://arxiv.org/pdf/1609.03286.pdf)

 

## Paper Connect

* NeuralDialogPapers[`github`](https://github.com/snakeztc/NeuralDialogPapers)

## Challenge

* [Musculoskeletal Radiographs (MURA)](https://stanfordmlgroup.github.io/competitions/mura/)

  

* [The Conversational Intelligence Challenge](http://convai.io/)

  

* [Two Sigma: Using News to Predict Stock Movements](https://www.kaggle.com/c/two-sigma-financial-news?from=timeline&isappinstalled=0#description)

  

## Blog

* [Domain-specific Knowledge Graph](https://mp.weixin.qq.com/s/aoYbTIoLt2UG-c8N9lNanQ)

  

* [Reptile: A Scalable Meta-Learning Algorithm](https://blog.openai.com/reptile/)

  

* [Universal Transformers](https://ai.googleblog.com/2018/08/moving-beyond-translation-with.html?m=1)

  

* [解决关系推理，从图网络入手！DeepMind图网络库开源](https://mp.weixin.qq.com/s?__biz=MzI3MTA0MTk1MA==&mid=2652029046&idx=3&sn=c3b2e74bade90ad3070f8a215e59ae1f&chksm=f121b487c6563d918eda53a1784ad0638148fe2a285bae533dccd763d2500477debefcdb6fa0&mpshare=1&scene=1&srcid=1019ykk2bhyJUcxLSBuiKDpJ#rd)

## News

* [人工智能将替代人力？](https://mp.weixin.qq.com/s?__biz=MzI5NzM4MzY3NA==&mid=2247485226&idx=1&sn=50f4152b12066f14c7176d9dcd6d7382&chksm=ecb4a4bfdbc32da91e7e1ea7d8505fbd6775bc4ed9550dc2d1abcfcc5793baf26445ee2442d8&mpshare=1&scene=1&srcid=0905eLsjZ1hH16ulksTSJNeZ#rd)

* [NIPS 2018丨解读微软亚洲研究院10篇入选论文](https://www.toutiao.com/i6598481166263648782/?tt_from=weixin_moments&utm_campaign=client_share&wxshare_count=3&from=groupmessage&timestamp=1536369175&app=news_article&utm_source=weixin_moments&isappinstalled=0&iid=43039538819&utm_medium=toutiao_ios&group_id=6598481166263648782&pbid=6578657786972259843)

* [资源 | 让AI学会刨根问底和放飞自我，斯坦福最新问答数据集CoQA](https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650748340&idx=2&sn=698d95c59f523ab627b465666932675e&chksm=871af1cab06d78dcf24d54977a92ebf6e90cdba7f76cfb4914db355d25afc3dde3cd8a43e015&mpshare=1&scene=1&srcid=0911JhbB1aS9zdqce2VWnLKG#rd)

* [MICCAI2018吸睛文集](https://mp.weixin.qq.com/s?__biz=MzU5OTczMDk3NA==&mid=2247483718&idx=1&sn=6eb5da934fd28e0096b8c8b664a3645f&chksm=feb13f37c9c6b621e89fa37aa096d6df24fb47fedc5a5db428a8fc5f222f7e11a3ba90d20b85&mpshare=1&scene=1&srcid=0928NUGfitj9rnoazeXVsaWk#rd)

* [MIT研发神经网络模型，可通过处理采访的原始文本和音频数据，判断抑郁倾向](https://mp.weixin.qq.com/s?__biz=MzAxMzc2NDAxOQ==&mid=2650368952&idx=1&sn=b06a8ca3c426a714b50ef6bb477648cf&chksm=83905f64b4e7d67298462f5057695909b51ac892a8571f309edc0296595b21f07c577479f7ad&mpshare=1&scene=1&srcid=0921PCGtzOhNFCgg8CqEEXg1#rd)

* [开复老师、AI Challenger携手进校园](https://mp.weixin.qq.com/s?__biz=MzU2ODM2OTQ1MQ==&mid=2247483997&idx=1&sn=e446cabdaa44109a4775dd6179fb7a1c&chksm=fc8e4a19cbf9c30fb211c404e438db354f94dd0f4ab8924fd94c253e224d48b4e813612227d3&mpshare=1&scene=1&srcid=1009OWNlopGDsb4eLQC3X9oR#rd)

* [谷歌BERT模型狂破11项纪录，全面超越人类](https://mp.weixin.qq.com/s?__biz=MzI3MTA0MTk1MA==&mid=2652028621&idx=1&sn=5366f2a95bc19862af2c4bbd468ccc19&chksm=f121ca3cc656432aeb0a8ed60bd60a18ffaa977965bc4b8166f70dc119c34bc02094c3905256&mpshare=1&scene=1&srcid=1013Yqp5ngQd7LRqMF8brFhC#rd)

* [CVPR2019专属，免费GPU机时开放中](https://mp.weixin.qq.com/s?__biz=MzI5Nzk5NjYyOA==&mid=2247484273&idx=1&sn=5ad46c075e6e6398afc3192d7f8fc0b9&chksm=ecaddb4adbda525c750e87d077704cfa224ac8a8d534704792697f83f49fda1b8efb5a1bbed4&mpshare=1&scene=1&srcid=10174NGMpJ1l0GipiZAuVybE#rd)

* [清华大学电子系吴及等在智慧医疗领域取得重要进展](http://news.tsinghua.edu.cn/publish/thunews/10303/2018/20181022085238111884562/20181022085238111884562_.html?from=singlemessage&isappinstalled=0)

* [只对你有感觉：谷歌用声纹识别实现定向人声分离](https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650750181&idx=5&sn=96d85740cb3d696cd2833f35f7908a6b&chksm=871af89bb06d718dfeea19c7baf58925eb9c5e35494bf3e9ec9b4a44b9af20cccf7287e1d57a&mpshare=1&scene=1&srcid=1027bxRyIdrewYFSrqF8WXmn#rd)

* [“AI临床医生”在败血症治疗中超越人类医生](https://mp.weixin.qq.com/s?__biz=MzAwNTAyMDY0MQ==&mid=2652556118&idx=2&sn=8023adeaedcf5906a6058a446cdb42df&chksm=80cd65d8b7baecce92ceb0d67335e77bbc63713bb3ab92a20bf9f8f13b51105c90afbf6c9a1b&mpshare=1&scene=1&srcid=102400zVTGyHhjlAezB05x7i#rd)

## Slides

* [Extracting and Modeling Relations with Graph Convolutional Networks](http://202.116.81.74/cache/12/03/www.akbc.ws/c98bae8bd49116c3dcc9546922d94064/ivan-titov-slides.pdf)

 

 

## Website

* [National Institutes of Health](https://www.nih.gov/news-events/news-releases/nih-clinical-center-releases-dataset-32000-ct-images)

 

* [buoyhealth](https://www.buoyhealth.com/symptoms-a-z/)

 

* [HUGGING FACE](https://huggingface.co/)

 

 

 

## Online Resources

* [Open QA](https://github.com/thunlp/OpenQA)

* [thunlp NRE]( https://github.com/thunlp/NRE)

 

* [thunlp JointNRE](https://github.com/thunlp/JointNRE)

 

* [VIPL](http://vipl.ict.ac.cn/view_database.php?id=14)

 

* [Pytorch tutorials](https://pytorch.org/tutorials/)

* [腾讯AI Lab开源800万中文词的NLP数据集](https://ai.tencent.com/ailab/nlp/embedding.html)

* [疾病知识图谱提取_工具库](https://rasa.com/)

* [中文知识图谱LTP库](https://www.ltp-cloud.com/)

* [DeepMind开源强化学习库“松露”](https://github.com/deepmind/trfl/)

* [DeepMind开源强化学习库“多巴胺”](https://github.com/google/dopamine)

* [Visual Turing Test](http://visualturingtest.org/)

 

* [Machine Common Sense Concept Paper](https://hcp-medical-1257456740.cos.ap-guangzhou.myqcloud.com/research%20source/1810.07528.pdf)

 

* [Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction](https://hcp-medical-1257456740.cos.ap-guangzhou.myqcloud.com/research%20source/EHR%40UCLA.pdf)

 

* [张铭知识学习和计算推理.pdf](https://hcp-medical-1257456740.cos.ap-guangzhou.myqcloud.com/research%20source/%E5%BC%A0%E9%93%AD%E7%9F%A5%E8%AF%86%E5%AD%A6%E4%B9%A0%E5%92%8C%E8%AE%A1%E7%AE%97%E6%8E%A8%E7%90%86.pdf)

 

* [钟宛君-Improving question answering by commonsense-show.pdf](https://hcp-medical-1257456740.cos.ap-guangzhou.myqcloud.com/research%20source/%E9%92%9F%E5%AE%9B%E5%90%9B-Improving%20question%20answering%20by%20commonsense-show.pdf)

* [GitHub超过2600星的PyTorch清单，涵盖CV/NLP等方向](https://mp.weixin.qq.com/s?__biz=MzUxNjcxMjQxNg==&mid=2247485683&idx=1&sn=1baa60c47cb17e3950ce3ca5f5dc83af&chksm=f9a27a7cced5f36adc1b17f908bc960a5116458cba25cec46ce0737302d0b7bd63d9810cf38e&mpshare=1&scene=1&srcid=1022KrRe1YEZPscvH5WiSUqM#rd)

 

## Online Courses

* [MILA 2018夏季深度学习与强化学习课程](https://dlrlsummerschool.ca/)

## Datasets

* [Medical segmentation decathlon](http://medicaldecathlon.com/)

* [BRATS 2018](https://www.med.upenn.edu/sbia/brats2018/data.html)

* [MultiTurnResponseSelection](https://github.com/MarkWuNLP/MultiTurnResponseSelection)

## Related Awesome Lists

* [Awesome-Deepbio](https://github.com/gokceneraslan/awesome-deepbio)

* [Awesome-Medical-Imaging](https://github.com/seokkim/Awesome-Medical-Imaging)

* [Awesome GAN for Medical Imaging](https://github.com/xinario/awesome-gan-for-medical-imaging)

* [Deep Learning Papers on Medical Image Analysis](https://github.com/albarqouni/Deep-Learning-for-Medical-Applications)

## License

[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)