Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method

Sirikorn Sangchocanonta; Pakinee Pooprasert; Nichapa Lerthirunvibul; Kanyarak Patchimnan; Phongphan Phienphanich; Adirek Munthuli; Sujittra Puangarom; Rath Itthipanichpong; Kitiya Ratanawongphaibul; Sunee Chansangpetch; Anita Manassakorn; Visanee Tantisevi; Prin Rojanapongpun; Charturong Tantibundhit

doi:10.1371/journal.pone.0314111

Peer Review History

Original SubmissionDecember 18, 2023
18 Dec 2023 Author Response https://doi.org/10.1371/journal.pone.0314111.r001
27 Feb 2024 Decision Letter - Tariq Mahmood Khan, Editor PONE-D-23-38529Optimizing Deep Learning Models for Glaucoma Screening with Vision Transformers for Resource Efficiency and the Pie Augmentation MethodPLOS ONE Dear Dr. Itthipanichpong, 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 Apr 12 2024 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. 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Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories. You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible. Please update your Data Availability statement in the submission form accordingly. [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: Yes ****** 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: Yes ****** 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: Yes ****** 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 work seems satisfactory, practical significance, innovation, novelty, scientific contribution , presented novel and better results and the work has the potential for acceptance and publication. Sufficient experiments have also been performed to validate the approach. It can be accepted after the incorporation given below certain suggestions properly. 1. Please discuss the time complexity of these DL models . 2. Please discuss the actual time taken by these DL models (in seconds). 3. Compare the results with prior state-of-the-art studies. 4. These recent state-of-the-art studies on glaucoma prediction should be discussed in the background, (Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets;Collaboration of features optimization techniques for the effective diagnosis of glaucoma in retinal fundus images;Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images;Emperor penguin optimization algorithm-and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images) 5 . Please perform ablation study 6. Please share the dataset on any public repository and share the link so that other researchers can also validate their latest . Regards, Reviewer #2: The manuscript titled 'Optimizing Deep Learning Models for Glaucoma Screening with Vision Transformers for Resource Efficiency and the Pie Augmentation Method' is well-written overall. However, I have identified a few concerns that warrant attention. 1. The introduction sets a good foundation for the paper's objectives, but it could benefit from expanding on related work. It would be helpful to include references to studies like "Screening of Glaucoma disease from retinal vessel images using semantic segmentation," "A Review on Glaucoma Disease Detection Using Computerized Techniques," and "CDED-Net: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening" to give readers a broader context and show how this research fits into the existing body of work. 2. While the paper focuses on developing lightweight networks, it overlooks some important aspects. Specifically, there's a lack of detailed analysis on trainable parameters compared to existing methods. It would be valuable to include this analysis to demonstrate the efficiency gains achieved by the proposed approach. Additionally, examining training and testing times would give readers a better understanding of the practical implications of the research. By addressing these points, the paper could provide a more comprehensive evaluation of its contributions. ****** 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: Yes: Munish Khanna 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.0314111.r002
Revision 1
21 Apr 2024 Author Response March 19, 2024 PLOS ONE Dear Reviewers, Thank you for taking the time to review our manuscript and for providing constructive feedback. We appreciate your positive remarks regarding the practical significance, innovation, novelty, scientific contribution, and potential for acceptance of our work. We have carefully reviewed your comments and suggestions and have addressed each one as outlined below: Reviewer #1 1. Please discuss the time complexity of these DL models: In Section 2.3, we have included a detailed discussion on the time complexity of the deep learning (DL) models used in our study. We evaluated the time complexity of the ConvNeXt-S and DeiT-S models. Critical factors such as the computational resources required for training and their efficiency are reflected in this evaluation. As shown in Fig. 1, we illustrated the efficiency of each model in terms of ImageNet-1K accuracy and Giga floating point operations per second (GFLOPs) using the calflops library. ConvNeXt-S achieves a higher accuracy of 83.1%, potentially due to its complex architecture, comprising 50M parameters. However, such complexity demands more computational resources, resulting in higher time complexity and requiring 8.7 GFLOPs for inference. In contrast, DeiT-S prioritizes efficiency with its simpler architecture, consisting of 22M parameters. This results in a lower time complexity of 4.6 GFLOPs and a slightly lower accuracy of 79.8%. This compromise suggests that DeiT-S, while exhibiting slightly lower accuracy, offers a more efficient utilization of computational resources. Therefore, DeiT-S presents a particular advantage in resource-constrained environments where computational efficiency is of paramount importance. 2. Please discuss the actual time taken by these DL models (in seconds): We evaluated the real-world performance of the ConvNeXt-S and DeiT-S models. By measuring the actual time taken to complete the practical test of classifying a single fundus image and producing a heat map, we gain insights into their efficiency and suitability for deployment. Table 8 displays the results of the performance test conducted on both central processing unit (CPU) and graphics processing unit (GPU). The test on GPU portrays that ConvNeXt-S takes approximately 2 seconds, while DeiT-S takes merely 0.2 seconds, offering a tenfold reduction in the actual time required for classifying results and generating heat maps. On the CPU, both ConvNeXt-S’ and DeiT-S’ ability to complete tasks in less than a second, with DeiT-S being faster. The test on CPU is especially applicable for virtual machines in hospitals across various regions where resource consideration is critical. Hence, our proposed approach offers a significant reduction in computational time, enhancing the models' capability to classify results for individual fundus images expeditiously. 3. Compare the results with prior state-of-the-art studies: As shown in Table 7, our evaluation ensured that the proposed method was not trained on the DRISHTI-GS1 test set. This approach allows us to determine how well the model performs on unseen data. This allows us to compare our work's performance to previous studies and assess its generalizability. Our results are promising, achieving an F1-score of 93.33%, accuracy of 90.20%, and sensitivity of 93.11%. While there is a chance at improvement, these results indicate the generalizability and potential of our method for real-world applications in glaucoma classification. 4. Discussion of recent state-of-the-art studies, including references to related work: Following the reviewer's suggestion, we have added the introduction section with a discussion on recent studies related to glaucoma prediction. We have specifically addressed the four studies you mentioned to highlight the effectiveness of outcomes from optimized classification. 5. Ablation Study: Thank you for your suggestion. We are going to conduct an ablation study in the future to gain a deeper understanding of the inner workings of the model. This approach involves systematically removing or modifying individual components within the transformer architecture such as layers, attention mechanisms, or even specific subsets of training data. Our objective is to isolate and identify the impact of each component on the model’s overall performance and decision-making process. 6. Dataset Sharing: We recognize the value of data sharing for scientific progress and allowing the research community to verify findings. Unfortunately, due to ethical agreements with the hospital providing the data, we cannot publicly share it. This agreement restricts data use to our team to safeguard patient privacy and uphold ethical standards. Despite this, we have made every effort to describe our dataset and methodologies in detail within our manuscript to ensure as much transparency as possible. In consideration of this, we are committed to providing a minimal dataset upon request, which includes study IDs and predictions from our proposed model. Researchers interested in accessing this dataset can contact the IRB human research office at King Chulalongkorn Memorial Hospital, Thai Red Cross Society, using the following information: - Contact: IRB Office, 3rd Floor, Anandamahidol Building - Telephone: 0 2256 4493 / 098 573 7622 - Email: medchulairb@chula.ac.th - Website: https://irb.md.chula.ac.th Furthermore, we have conducted additional testing using the DRISHTI-GS1 public dataset, which our models have never been trained on. Testing on diverse datasets strengthens our findings' generalizability and validates our research. Reviewer #2 1. Inclusion of references to related work: We have carefully reviewed the literature and incorporated references to the studies you suggested. These references are now included in a paragraph within the introduction section, which mentions that AI can automate various tasks such as cup and disc segmentation, vertical cup-to-disc ratio calculation in the optic nerve head (ONH), and relevant feature extraction. 2. The need for a more comprehensive evaluation of lightweight networks: In Section 2.3, we analyze the trainable parameters of our proposed model compared to existing methods to demonstrate its efficiency. Additionally, we discuss both the theoretical training times in Section 2.3 and the actual time taken for testing in the discussion and conclusion sections, providing a comprehensive picture of the model's computational requirements. Finally, we really appreciated your valuable feedback and considering our work for publication. Sincerely, Rath Itthipanichpong (corresponding author) E-mail: doctorrath@gmail.com Attachments Attachment Submitted filename: res_reviews_new_2.pdf https://doi.org/10.1371/journal.pone.0314111.r003
6 Nov 2024 Decision Letter - Muhammad Mateen, Editor Optimizing Deep Learning Models for Glaucoma Screening with Vision Transformers for Resource Efficiency and the Pie Augmentation Method PONE-D-23-38529R1 Dear Dr. Itthipanichpong, 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 will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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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 #3: 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 #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #3: Yes ****** 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 #3: No ****** 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 #3: 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: This revised version can be updated now. Congratulations to all authors !! ********************************************************************************* Reviewer #3: Previous reviewers observations have been addressed and these have been highlighted by the authors. I believe the article can now accepted for publication, ****** 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: Yes: Munish Khanna Reviewer #3: No ******** https://doi.org/10.1371/journal.pone.0314111.r004
Formally Accepted
Acceptance Letter - Muhammad Mateen, Editor PONE-D-23-38529R1 PLOS ONE Dear Dr. Itthipanichpong, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, 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 customercare@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. Muhammad Mateen Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0314111.r005

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