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

doi:10.1371/journal.pone.0262209

Peer Review History

Original SubmissionJune 21, 2021
19 Aug 2021 Decision Letter - Yifan Peng, Editor PONE-D-21-15198 Attention Based Automated Radiology Report Generation using CNN and RNN PLOS ONE Dear Dr. Paracha, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== The proposed method needs to compare with other state-of-the-art methods, such as https://arxiv.org/abs/1912.08226 https://arxiv.org/abs/2010.10042 The experiments need to conduct on several datasets to demonstrate the model's generalizability. The paper needs to be proofread. There are many typos and errors. ============================== Please submit your revised manuscript by Oct 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. 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However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: “This research project was funded by the Deanship of Scientific Research, Princess Nourah bint Abdulrahman University, through the “Program of Research Project Funding After Publication, grant No (42-PRFA-P-53)”. https://www.pnu.edu.sa/en/Pages/home.aspx No, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.” Please include your amended statements within your cover letter; we will change the online submission form on your behalf. Additional Editor Comments (if provided): [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: N/A ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: No ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The author proposed an attention based Automated Radiology Report Generation using CNN and RNN. The paper is easy to read and the results can confirm their idea. But there are still several issues that need to be addressed. (1) A line is missing at the bottom of the table. (2) The equation 2 is missing a ). (3) The second Table 1 should be Table 2 and the Table 2 should be Table 3. (4) The resolution of Figure 3 is low. (5) The reference seems wrong. For example, in the section 4.3 Baselines: The proposed model is compared with many current best performing architectures, for example, LRCN [18] by Donahue et al., Soft ATT [19] by Xu et al., ATT-RK [20] by You et al., and Hieratical Generation [21] by Krause, et al. But in the Table 2 the references are 19-22. You have to check your references carefully. (6) You need to include the latest method to compare with your proposed method, such as Meshed-Memory Transformer for image captioning, Linguistics Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation. You can also compare with other methods, but at least those proposed in 2020. Reviewer #2: This manuscript describes an attention-based report generation from X-ray using CNN and RNN. Descriptions of x-ray images were missing from both data and experiment sections. More details on the images will be helpful (size of x-ray, divided image into n parts? what is n in the experiment?) VGG was not elaborated on Page 12. The experiments seem a bit confusing. For example, when LSTM and RNN were used in the experiment? Is it CNN-RNN or CNN-LSTM? In text, it is mentioned CNN-RNN, but in figure, it is CNN-LSTM. The text of the manuscript can be reduced significantly. Please provide all the parameters for all the algorithms. Captions for images are not available. Problems in table caption. The numbering of tables is wrong. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0262209.r001
Revision 1
24 Sep 2021 Author Response Editor’s Comments & Responses 1. The proposed method needs to compare with other state-of-the-art methods, such as 2. https://arxiv.org/abs/1912.08226 3. https://arxiv.org/abs/2010.10042 Response: We have considered both articles and added some more relevant ones in revised version. We have also compared the results of proposed technique with these articles. Following recent articles have been added in revised version • Alfarghaly, O., Khaled, R., Elkorany, A., Helal, M., & Fahmy, A. (2021). Automated radiology report generation using conditioned transformers. Informatics in Medicine Unlocked, 24, 100557. • Liu, G., Hsu, T. M. H., McDermott, M., Boag, W., Weng, W. H., Szolovits, P., & Ghassemi, M. (2019, October). Clinically accurate chest x-ray report generation. In Machine Learning for Healthcare Conference (pp. 249-269). PMLR. • Chen, Z., Song, Y., Chang, T. H., & Wan, X. (2020). Generating radiology reports via memory-driven transformer. arXiv preprint arXiv:2010.16056. • Miura, Y., Zhang, Y., Tsai, E. B., Langlotz, C. P., & Jurafsky, D. (2020). Improving factual completeness and consistency of image-to-text radiology report generation. arXiv preprint arXiv:2010.10042. 2. The experiments need to conduct on several datasets to demonstrate the model's generalizability. Response: We have added one more publically available dataset for experimentations and results are added in revised article. We have added more results and also have highlighted failure cases to show model’s generalizability. Please refer to table-3, table-5 and figure 9. 3. The paper needs to be proofread. There are many typos and errors. Response: The article was initially proof read by a third party EdiTag. We have again proofread it for possible errors. Reviewer's Comments & Responses 1. Is the manuscript technically sound, and do the data support the conclusions? Reviewer #1: Yes Reviewer #2: Yes 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: N/A 3. Have the authors made all data underlying the findings in their manuscript fully available? Reviewer #1: Yes Reviewer #2: No Response: The datasets used in this article are publically available 4. Is the manuscript presented in an intelligible fashion and written in standard English? Reviewer #1: Yes Reviewer #2: No Response: Manuscript is updated according to the comments of the reviewers and academic Editor and all the grammatical and typographical errors are catered. It has been proof read by third party. 5. Review Comments to the Author Reviewer #1 The author proposed an attention based Automated Radiology Report Generation using CNN and RNN. The paper is easy to read and the results can confirm their idea. But there are still several issues that need to be addressed. Response: We are thankful to the reviewer for encouragement and also sharing his comments to improve the quality of our article (1) A line is missing at the bottom of the table. Response: We have updated the formatting of all tables and bottom lines are added at the end of all tables. We have also updated captions of all tables for better understanding. (2) The equation 2 is missing a ). Response: The equation is updated and ‘)’ is added at the end of equation 2. (3) The second Table 1 should be Table 2 and the Table 2 should be Table 3. Response: We have corrected table numbering. (4) The resolution of Figure 3 is low. Response: We have updated Figure 3 to improve its resolution. We have also improved the resolution of other figures. (5) The reference seems wrong. For example, in the section 4.3 Baselines: The proposed model is compared with many current best performing architectures, for example, LRCN [18] by Donahue et al., Soft ATT [19] by Xu et al., ATT-RK [20] by You et al., and Hieratical Generation [21] by Krause, et al. But in the Table 2 the references are 19-22. You have to check your references carefully. Response: References are updated and now all the references are properly aligned. (6) You need to include the latest method to compare with your proposed method, such as Meshed-Memory Transformer for image captioning, Linguistics Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation. You can also compare with other methods, but at least those proposed in 2020. Response: As per reviewer suggestion, we have conducted more experiments and compared our results with more recent articles. We have also considered more public datasets like MIMIC for experiments and comparisons. Reviewer #2: This manuscript describes an attention-based report generation from X-ray using CNN and RNN. Descriptions of x-ray images were missing from both data and experiment sections. More details on the images will be helpful (size of x-ray, divided image into n parts? what is n in the experiment?) Response: We have added these details with respect to both dataset and experimental setup in section 4.2 of revised article. VGG was not elaborated on Page 12. Response: Section 3.1 of revised article contains details related to VGG and we have also shown all layers of VGG in figures 2 and 3. The Softmax layer of VGG16 was replaced with the final 1 × 4096 FC layer. This layer now acts as an input to the decoder as well as the generation of medical reports. The output of the VGG16 network is a vector of size 1 × 4096, which will later be converted into a fixed vector length of 1 × 256 that is used to represent the features of the images. The experiments seem a bit confusing. For example, when LSTM and RNN were used in the experiment? Is it CNN-RNN or CNN-LSTM? In text, it is mentioned CNN-RNN, but in figure, it is CNN-LSTM. Response: Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks (RNN) which makes it easier to remember past data in memory and considered best of the text generation tasks. The vanishing gradient problem of RNN is resolved here. So as a big picture we write RNN every where even in the title of the paper. But actually, we are using the best model of RNN which is LSTM. But as highlighted by the reviewer, we have replaced RNN with LSTM wherever we are mentioning proposed technique. We have also updated the title accordingly. The text of the manuscript can be reduced significantly. Response: As advised by the reviewer, we have reduced the text in introduction and literature review sections. However, more results and discussions are added to emphasize more on proposed system robustness. So in general the length of article is almost same. Please provide all the parameters for all the algorithms. Response: We have added parameters in figure 2,3 and 4 of revised article. We have also added table 2 to show hyper parameters. Captions for images are not available. Report: As per journal formatting, we didn’t add figures in the text. However the image captions are added at relevant places for better understanding. Problems in table caption. The numbering of tables is wrong. Response: We have corrected table numbering and have also improved the captions for better understanding. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0262209.r002
8 Nov 2021 Decision Letter - Yifan Peng, Editor PONE-D-21-15198R1Attention Based Automated Radiology Report Generation using CNN and LSTMPLOS ONE Dear Dr. Paracha, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ==============================1. please briefly describe the baseline methods 2. please check figure 4 3. please make sure the codes are publicly available. ============================== Please submit your revised manuscript by Dec 23 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Yifan Peng, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: All my previous comments have been addressed. I have one more comments for this revised version. As the author have conducted more experiments and compared their results with more recent articles, they need added the new comparison methods to the Section Baselines. Reviewer #2: The authors addressed all the comments. Figure 4 is black and there is nothing. The authors may share the code. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. 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Revision 2
23 Nov 2021 Author Response Comment: Please briefly describe the baseline methods Response: We have added brief description of baseline methods in section “4.3 Baseline” of revised article. Comment: Please check figure 4 Response: Figure 4 is updated. Now it is properly visible. Comment: Please make sure the codes are publicly available. Response: We have made our complete code publically available at our research group website. It can be accessed at http://biomisa.org/index.php/downloads/ Attachments Attachment Submitted filename: Response File.docx https://doi.org/10.1371/journal.pone.0262209.r004
16 Dec 2021 Decision Letter - Yifan Peng, Editor PONE-D-21-15198R2Attention Based Automated Radiology Report Generation using CNN and LSTMPLOS ONE Dear Dr. Paracha, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jan 30 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Yifan Peng, Ph.D. Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments (if provided): 1. Figure 9 on page 39 is black 2. Figure 4 on page 40 is black 3. I feel the table on page 12 is not Fig 4. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0262209.r005
Revision 3
18 Dec 2021 Author Response We have made all updates with respect to figures mentioned by the editor Attachments Attachment Submitted filename: Response File-II.docx https://doi.org/10.1371/journal.pone.0262209.r006
20 Dec 2021 Decision Letter - Yifan Peng, Editor Attention Based Automated Radiology Report Generation using CNN and LSTM PONE-D-21-15198R3 Dear Dr. Paracha, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Yifan Peng, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0262209.r007
Formally Accepted
27 Dec 2021 Acceptance Letter - Yifan Peng, Editor PONE-D-21-15198R3 Attention based Automated Radiology Report Generation using CNN and LSTM Dear Dr. Paracha: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Yifan Peng Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0262209.r008

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