Web scraping system for extracting medical-like AI conference papers.
IJCAI, NIPS, ICML, ICLR, IJCNN
CVPR, ICCV
ACL, NAACL, EMNLP, CoNLL, COLING, IJCNLP, EACL, LREC, CL, SEMEVAL, TACL, ALTA, HLT, JEP-TALN-RECITAL, MUC, PACLIC, RANLP, ROCLING-IJCLCLP, TINLAP, TIPSTER
Import module:
import medical.aiTo get medical-like AI papers in ACL 2019, create a Query instance and use .search() method:
query = medical.ai.Query(conference='cvpr', year=2018)
result = query.search()
With titles and URLs on standard output, you obtain information of papers as a list of Article object:
Connecting for CVPR 2018 ...
Searching... 13 match / 980
===================================
Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria.
http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Clinical_Skin_Lesion_CVPR_2018_paper.html
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation.
http://openaccess.thecvf.com/content_cvpr_2018/html/Dalca_Anatomical_Priors_in_CVPR_2018_paper.html
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Medical-like AI papers in CVPR 2018: 13 / 980
===================================
for article in result[0]:
print(article.title)
print(article.url)
Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria.
http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Clinical_Skin_Lesion_CVPR_2018_paper.html
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation.
http://openaccess.thecvf.com/content_cvpr_2018/html/Dalca_Anatomical_Priors_in_CVPR_2018_paper.html
If you want the result to be more quiet, use config as this:
config = medical.ai.Config(quiet=True)
articles = query.search(config)
Run medical_ai.py on the shell:
python3 medical/ai.py <CONFERENCE> <YEAR>
e.g. For 'nips 2018', you get 11 medical-like conference papers:
python3 medical/ai.py nips 2018
Connecting...
Searching... 11 matches / 1011
===================================
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.
http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation
Representation Learning for Treatment Effect Estimation from Observational Data.
http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data
Lifelong Inverse Reinforcement Learning.
http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.
http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare
Mental Sampling in Multimodal Representations.
http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.
http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.
http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks
Does mitigating ML's impact disparity require treatment disparity?
http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity
HOUDINI: Lifelong Learning as Program Synthesis.
http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.
http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.
http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies
===================================
Medical-like AI papers in NIPS 2018: 11 / 1011
===================================
Multiple conferences and years are also available:
python3 medical/ai.py <CONFERENCE1> <CONFERENCE2> ... <YEAR1> <YEAR2> ...
e.g. For ACL 2018 & ACL 2019 & NAACL 2018 & NAACL 2019, input python3 medical/ai.py acl naacl 2018 2019.
Use --html option to display result as HTML link tags:
python3 medical/ai.py nips 2018 --html
Connecting...
Searching... 11 match / 1011
===================================
<a href="http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation" target="_blank" alt="Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.">Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.</a><br/>
<a href="http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data" target="_blank" alt="Representation Learning for Treatment Effect Estimation from Observational Data.">Representation Learning for Treatment Effect Estimation from Observational Data.</a><br/>
<a href="http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning" target="_blank" alt="Lifelong Inverse Reinforcement Learning.">Lifelong Inverse Reinforcement Learning.</a><br/>
<a href="http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare" target="_blank" alt="MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.">MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.</a><br/>
<a href="http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations" target="_blank" alt="Mental Sampling in Multimodal Representations.">Mental Sampling in Multimodal Representations.</a><br/>
<a href="http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis" target="_blank" alt="REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.">REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.</a><br/>
<a href="http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks" target="_blank" alt="Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.">Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.</a><br/>
<a href="http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity" target="_blank" alt="Does mitigating ML's impact disparity require treatment disparity?">Does mitigating ML's impact disparity require treatment disparity?</a><br/>
<a href="http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis" target="_blank" alt="HOUDINI: Lifelong Learning as Program Synthesis.">HOUDINI: Lifelong Learning as Program Synthesis.</a><br/>
<a href="http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data" target="_blank" alt="Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.">Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.</a><br/>
<a href="http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies" target="_blank" alt="Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.">Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.</a>
===================================
Medical-like AI papers in NIPS 2018: 11 / 1011
===================================
Use --markdown option to display result as markdown links:
python3 medical/ai.py nips 2018 --markdown
Connecting...
Searching... 11 match / 1011
===================================
[Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.](http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation)
[Representation Learning for Treatment Effect Estimation from Observational Data.](http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data)
[Lifelong Inverse Reinforcement Learning.](http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning)
[MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.](http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare)
[Mental Sampling in Multimodal Representations.](http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations)
[REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.](http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis)
[Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.](http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks)
[Does mitigating ML's impact disparity require treatment disparity?](http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity)
[HOUDINI: Lifelong Learning as Program Synthesis.](http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis)
[Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.](http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data)
[Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.](http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies)
===================================
Medical-like AI papers in NIPS 2018: 11 / 1011
===================================
Use --copy option to copy result onto clipboard:
python3 medical/ai.py nips 2018 --markdown --copy
Connecting...
Searching... 11 match / 1011
===================================
[Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation.](http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation)
[Representation Learning for Treatment Effect Estimation from Observational Data.](http://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data)
[Lifelong Inverse Reinforcement Learning.](http://papers.nips.cc/paper/7702-lifelong-inverse-reinforcement-learning)
[MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare.](http://papers.nips.cc/paper/7706-mime-multilevel-medical-embedding-of-electronic-health-records-for-predictive-healthcare)
[Mental Sampling in Multimodal Representations.](http://papers.nips.cc/paper/7817-mental-sampling-in-multimodal-representations)
[REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis.](http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis)
[Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks.](http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks)
[Does mitigating ML's impact disparity require treatment disparity?](http://papers.nips.cc/paper/8035-does-mitigating-mls-impact-disparity-require-treatment-disparity)
[HOUDINI: Lifelong Learning as Program Synthesis.](http://papers.nips.cc/paper/8086-houdini-lifelong-learning-as-program-synthesis)
[Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data.](http://papers.nips.cc/paper/8125-bayesian-multi-domain-learning-for-cancer-subtype-discovery-from-next-generation-sequencing-count-data)
[Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.](http://papers.nips.cc/paper/8193-life-long-disentangled-representation-learning-with-cross-domain-latent-homologies)
===================================
Medical-like AI papers in NIPS 2018: 11 / 1011
===================================
* * * Copied this result to clipboard * * *
To browse all available options, input python3 medical/ai.py -h or python3 medical/ai.py --help:
usage: medical/ai.py [-h] [-q] [--copy] [-m | --html]
[--title-only | --url-only]
conference year
++++++++++++++++++++++++++++++++++++++++++++++++++
Pickup medical AI paper titles and URLs from specified conference and year.
会議名と年数を指定すると, 医療に関連するAI論文のみを探し出してタイトルとURLを列挙します.
To get from ACL 2019, input like this: python3 medical/ai.py acl 2019
例えばACL 2019採択論文から探すには本プログラムを python3 medical medical/ai.py acl 2019 と実行してください.
Conference name is case insensitive.
会議名は大文字でも小文字でも構いません.
To output HTML link tags or markdown links, use options below.
以下に示すオプションを使うと, 結果をHTMLリンクタグやMarkdownリンクとして出力することも可能です.
++++++++++++++++++++++++++++++++++++++++++++++++++
positional arguments:
conferences_and_years
speficy conferences and years
example1: acl 2019
example2: acl naacl 2019
example3: acl 2018 2019
example4: acl naacl 2018 2019
optional arguments:
-h, --help show this help message and exit
-q, --quiet be more quiet
--copy copy result to clipboard
-a, --all get also non-medical AI papers
-m, --md, --markdown output as markdown links
collaborates with --url-only
ignores --title-only
--html output as HTML <a> tags
collaborates with --url-only
ignores --title-only
--title-only output paper title only
--url-only output paper URL only