Filters








14 Hits in 2.6 sec

TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays [article]

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Ronald M. Summers
<span title="2018-01-12">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Chest X-rays are one of the most common radiological examinations in daily clinical routines. Reporting thorax diseases using chest X-rays is often an entry-level task for radiologist trainees.  ...  We propose a novel Text-Image Embedding network (TieNet) for extracting the distinctive image and text representations.  ...  Thanks to Adam Harrison and Shazia Dharssi for proofreading the manuscript. We are also grateful to NVIDIA Corporation for the GPU donation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.04334v1">arXiv:1801.04334v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hi6gsn42ujc7rkm7fxqpkmomqe">fatcat:hi6gsn42ujc7rkm7fxqpkmomqe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200913230500/https://arxiv.org/pdf/1801.04334v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/17/7a/177a09f82c8d0cfc300bb16dd01736343ead873e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.04334v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays

Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Ronald M. Summers
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;
Chest X-rays are one of the most common radiological examinations in daily clinical routines. Reporting thorax diseases using chest X-rays is often an entry-level task for radiologist trainees.  ...  We propose a novel Text-Image Embedding network (TieNet) for extracting the distinctive image and text representations.  ...  Thanks to Adam Harrison and Shazia Dharssi for proofreading the manuscript. We are also grateful to NVIDIA Corporation for the GPU donation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2018.00943">doi:10.1109/cvpr.2018.00943</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/WangPLLS18.html">dblp:conf/cvpr/WangPLLS18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a24rm6k3tvf7fkyfm6obygpe4i">fatcat:a24rm6k3tvf7fkyfm6obygpe4i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190819024510/http://openaccess.thecvf.com:80/content_cvpr_2018/papers/Wang_TieNet_Text-Image_Embedding_CVPR_2018_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/2b/c72b063e23b8b45b57a42ebc2f9714297c539a6f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2018.00943"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Detection of Thoracic Diseases using Deep Learning

Salome Palani, Arya Kulkarni, Abishai Kochara, Kiruthika M, M.D. Patil, V.A. Vyawahare
<span title="">2020</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mzzzerkv2zd63fbnrpvy24xm54" style="color: black;">ITM Web of Conferences</a> </i> &nbsp;
Chest X-rays are currently the most common and globally used radiology practices for detecting thoracic diseases.  ...  Patients suffering from thoracic diseases need to take Chest X-Rays which are read by radiologists and a report is generated by them.  ...  ., proposed a novel Text Image Embedding network (Tienet) that extracts image and text representations distinctly.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/itmconf/20203203024">doi:10.1051/itmconf/20203203024</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/scpg6hl4vfdzxovyvmjldoiw64">fatcat:scpg6hl4vfdzxovyvmjldoiw64</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200818101427/https://www.itm-conferences.org/articles/itmconf/pdf/2020/02/itmconf_icacc2020_03024.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b9/a7/b9a7a90555db649c6dc8bbc327c429fbbe974122.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/itmconf/20203203024"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Deep learning in generating radiology reports: A survey

Maram Mahmoud A. Monshi, Josiah Poon, Vera Chung
<span title="2020-05-15">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7zekrk5bkbglxhzdjmkpl6hksq" style="color: black;">Artificial Intelligence in Medicine</a> </i> &nbsp;
So far, the most common approach has been to utilize publicly available datasets and develop DL models that integrate convolutional neural networks (CNN) for image analysis alongside recurrent neural networks  ...  This survey will be useful for researchers interested in DL, particularly those interested in applying DL to radiology reporting.  ...  frontal and lateral chest X-rays when detecting common thorax diseases.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.artmed.2020.101878">doi:10.1016/j.artmed.2020.101878</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32425358">pmid:32425358</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7227610/">pmcid:PMC7227610</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ccy2g2rh2zavdjjvvjlv7poxau">fatcat:ccy2g2rh2zavdjjvvjlv7poxau</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200529222503/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7227610&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/36/25/362506fe8e0a20fd60fe6b657004d1fd9e549cef.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.artmed.2020.101878"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7227610" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography

Thi Kieu Ho, Jeonghwan Gwak
<span title="2019-10-02">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
The accurate localization and classification of lung abnormalities from radiological images are important for clinical diagnosis and treatment strategies.  ...  We demonstrate that our approaches efficiently leverage interdependencies among target annotations and establish the state of the art classification results of 14 thoracic diseases in comparison with current  ...  Acknowledgments: The authors would like to note that some part of work was done when J.G. was in Seoul National University Hospital, and we express our gratitude for the valuable and constructive comments  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9194130">doi:10.3390/app9194130</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jzgpjzbsc5cfza47zlg5oocxhe">fatcat:jzgpjzbsc5cfza47zlg5oocxhe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210160151/https://res.mdpi.com/d_attachment/applsci/applsci-09-04130/article_deploy/applsci-09-04130-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e3/87/e387eff36dd1fe2c767ffd39994c8387e0a331f2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9194130"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

TMRGM: A Template-Based Multi-Attention Model for X-Ray Imaging Report Generation

Xuwen Wang, Yu Zhang, Zhen Guo, Jiao Li
<span title="">2021</span> <i title="Atlantis Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rls3zr3inrhyzksqgbjhfj7aha" style="color: black;">Journal of Artificial Intelligence for Medical Sciences</a> </i> &nbsp;
This paper aims to extract valuable information automatically from medical images to assist doctors in chest X-ray image interpretation.  ...  The rapid growth of medical imaging data brings heavy pressure to radiologists for imaging diagnosis and report writing.  ...  In particular, images concerning chest diseases, e.g., chest X-rays and chest CT scan are commonly used for clinical screening and diagnosis, and account for a large proportion in public datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jaims.d.210428.002">doi:10.2991/jaims.d.210428.002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tkp2o55xk5h4rcmfr4b3enpuou">fatcat:tkp2o55xk5h4rcmfr4b3enpuou</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210530080351/https://www.atlantis-press.com/article/125956175.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d0/75/d0755e4b21d5fd22477111ad8828634fe4583c48.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2991/jaims.d.210428.002"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Utilizing Knowledge Distillation in Deep Learning for Classification of Chest X-ray Abnormalities

Thi Kieu Khanh Ho, Jeonghwan Gwak
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
TieNet [39] was introduced to first classify ChestX-ray14 images by extracting distinctive X-ray images and embedded texts from corresponding reports; it was later converted into a chest Xray-reporting  ...  The appearance of a thorax disease is usually accompanied by other related diseases visible in chest X-ray images; for instance, pneumothorax is often associated with pneumonia.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3020802">doi:10.1109/access.2020.3020802</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ucyyrx3ijzft3he2kabn7tgqyi">fatcat:ucyyrx3ijzft3he2kabn7tgqyi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108120653/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09183910.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/49/c9/49c9fa5ad1bf1390787acf0a3e96ca58f0d7ef53.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3020802"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Combining Deep Learning and Knowledge-driven Reasoning for Chest X-Ray Findings Detection

Ashutosh Jadhav, Ken C L Wong, Joy T Wu, Mehdi Moradi, Tanveer Syeda-Mahmood
<span title="2021-01-25">2021</span> <i title="American Medical Informatics Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/g6q27b56yzeupjryv6lby2r2pa" style="color: black;">AMIA Annual Symposium Proceedings</a> </i> &nbsp;
In this work, we used a comprehensive chest X-ray findings vocabulary to automatically annotate an extensive collection of chest X-rays using associated radiology reports and a vocabulary-driven concept  ...  The annotated X-rays are used to train a deep neural network classifier for finding detection.  ...  The TieNet architecture is used for thorax disease classification and reporting in CXR.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33936433">pmid:33936433</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8075485/">pmcid:PMC8075485</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vlnraayghvegjli2kghaxrtpvq">fatcat:vlnraayghvegjli2kghaxrtpvq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210504043503/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8075485&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/27/06/270658f7bb2e797b0e3d66021c6b11e55ff68b80.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075485" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Attention based automated radiology report generation using CNN and LSTM

Mehreen Sirshar, Muhammad Faheem Khalil Paracha, Muhammad Usman Akram, Norah Saleh Alghamdi, Syeda Zainab Yousuf Zaidi, Tatheer Fatima, Yifan Peng
<span title="2022-01-06">2022</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients.  ...  Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task.  ...  Common chest diseases such as pneumonia, pneumothorax, and effusion [1] are diagnosed with the help of medical images, such as chest X-rays (CXR) and CT scans.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0262209">doi:10.1371/journal.pone.0262209</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34990477">pmid:34990477</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8736265/">pmcid:PMC8736265</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/swbsvyxfcna4hiftd3ezsr4dwm">fatcat:swbsvyxfcna4hiftd3ezsr4dwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220201025431/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0262209&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1d/b6/1db6843121e76acd3d6b48c19eb45fb34666ff66.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0262209"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8736265" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Part-Aware Mask-Guided Attention for Thorax Disease Classification

Ruihua Zhang, Fan Yang, Yan Luo, Jianyi Liu, Jinbin Li, Cong Wang
<span title="2021-05-23">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
simultaneously for thorax disease classification.  ...  Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions.  ...  Acknowledgments: We would like to acknowledge supports from the Chinese Academy of Medical Sciences and Peking Union Medical College.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23060653">doi:10.3390/e23060653</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34070982">pmid:34070982</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/275cahg34rd7hjw7tpmgzfdxli">fatcat:275cahg34rd7hjw7tpmgzfdxli</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210611011018/https://res.mdpi.com/d_attachment/entropy/entropy-23-00653/article_deploy/entropy-23-00653-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/18/26/182629dae1022c2fddf26410e79735cceccdcf15.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23060653"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Learning Semi-Structured Representations of Radiology Reports [article]

Tamara Katic, Martin Pavlovski, Danijela Sekulic, Slobodan Vucetic
<span title="2021-12-20">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We evaluated the proposed approach on the OpenI corpus of manually annotated chest x-ray radiology reports.  ...  Because the space of medical findings in radiology reports is vast and potentially unlimited, recent studies proposed mapping free-text statements in radiology reports to semi-structured strings of terms  ...  Tienet: Text-image embedding network for common thorax disease classification and re- porting in chest x-rays.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.10746v1">arXiv:2112.10746v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sfp4gnodvjepnfga6nidllyxym">fatcat:sfp4gnodvjepnfga6nidllyxym</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211227080716/https://arxiv.org/pdf/2112.10746v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/64/22/64227bc9897735f1f2fe042e61fb0dae5e5bde91.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.10746v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning [article]

Guangyi Liu, Yinghong Liao, Fuyu Wang, Bin Zhang, Lu Zhang, Xiaodan Liang, Xiang Wan, Shaolin Li, Zhen Li, Shuixing Zhang, Shuguang Cui
<span title="2021-08-11">2021</span> <i title="Institute of Electrical and Electronics Engineers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/j6amxna35bbs5p42wy5crllu2i" style="color: black;">IEEE Transactions on Neural Networks and Learning Systems</a> </i> &nbsp; <span class="release-stage" >pre-print</span>
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.  ...  In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan  ...  For example, TieNet [20] classifies the chest X-rays by using both image features and text embeddings and then transformed the framework into a chest X-ray reporting system. C.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2021.3099165">doi:10.1109/tnnls.2021.3099165</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34370672">pmid:34370672</a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.05067v1">arXiv:2108.05067v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pu37j3eddrcz5l534u3wahxir4">fatcat:pu37j3eddrcz5l534u3wahxir4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210814160540/https://arxiv.org/pdf/2108.05067v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e2/c6/e2c6734a1dc714afa1f56767dbf151feaab76be5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnnls.2021.3099165"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.05067v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
<span title="2020-12-04">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection  ...  Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area.  ...  For example, a Text-Image embedding network (TieNet) is designed to classify the common thorax disease in chest X-rays [105] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.12150v3">arXiv:2004.12150v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2cqumcjkizgivmo67reznxacie">fatcat:2cqumcjkizgivmo67reznxacie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930060724/https://arxiv.org/pdf/2004.12150v2.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/72/d472de5c448e05d5298800184cc0a2ed9291ee96.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.12150v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Joint Triplet Autoencoder for Histopathological Colon Cancer Nuclei Retrieval [article]

Satya Rajendra Singh, Shiv Ram Dubey, Shruthi MS, Sairathan Ventrapragada, Saivamshi Salla Dasharatha
<span title="2021-05-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Autoencoder and Siamese networks are two deep learning models to learn the latent space (i.e., features or embedding). Autoencoder works based on the reconstruction of the image from latent space.  ...  A joint supervised learning for Siamese network and unsupervised learning for Autoencoder is performed.  ...  The authors would like to thank NVIDIA Corporation for the support of 2 GeForce Titan X Pascal GPUs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.10262v2">arXiv:2105.10262v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ef3qifoujbgxpkpvbcx6a7ko2e">fatcat:ef3qifoujbgxpkpvbcx6a7ko2e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210528214347/https://arxiv.org/pdf/2105.10262v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/82/9d82fd7bc0628f2a219a3db5908dec232d4fb11c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.10262v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>