PONE-D-23-20689Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detectionPLOS ONE

Dear Dr. Koriakina,

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"This work is supported by: Sweden’s Innovation Agency (VINNOVA) https://www.vinnova.se/en/apply-for-funding/funded-projects/, grants 2017-02447 (J.L.) and 2020-03611 (J.L.), and the Swedish Research Council https://www.vr.se/english/swecris.html#/, grant 2017-04385(J.L.). A part of the computations was enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at Chalmers Centre for Computational Science and Engineering (C3SE), partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and no. 2018-05973. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."

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Additional Editor Comments:

Please revise and resubmit your manuscript.  

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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: Partly

Reviewer #3: Partly

Reviewer #4: Yes

Reviewer #5: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: No

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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

Reviewer #3: Yes

Reviewer #4: No

Reviewer #5: No

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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

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

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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 authors created a new database to use for detection of malign cells correlated with oral cancer (OC). Since annotation on the cell level seems near impossible, the authors augmented QMNIST to emulate the characteristics of malign cells.

Comments:

1) In the abstract, you mention "This dataset serves as a proxy...". This needs to be better explained and describe what you aim to achieve and how you use it.

2) Have the authors considered self-supervised methods? Usually there are improvements when combined with MIL approaches. Some results even with simple methods would benefit the paper.

3) line 58 "comprise" should be "include"

4) Phrase in lines 64-68 should be rewritten to convery the meaning in a more clear manner.

5) In lines 82-83 you write: "A main advantage of PAP-QMNIST is that is offers access to reliable GT annotation at the instance (cell) level". In the previous sentence, you write that GT annotation is not feasible. How both are true? Giving a brief overview of the dataset you created and what you aim to achieve would be beneficial here. Moreover, being more "precise" in the language would avoid confusion (it seems that PAP-QMNIST does not have cells at all).

6) In the end of Introduction, I believe "splitting" the contributions and findings, as well as adding a small overview of your findings would be beneficial for the reader (to have a first impression on what to expect from the rest of the paper).

7) Is there a reason for using F1 score instead of metrics like FAR, FRR? These metrics are interpreted in a manner that is easier to associate a cost for each action. An input of a medical expert, e.g. we are interested in very low number of False Negatives, would also add value to your findings.

8) In section "Considerd MIL method" some description/refresher would help the flow of the paper. For example, what is the modified version described in [13] (again a short description and the reader can refer to [13] for more details)? But throught the paper there isn't any model description.

9) In line 177 you mention 24 patients, and in lines 185-186 you give some extra information. It would help the reader

if you transferred the info in 185-186 in line 177.

10) In line 194 you write: "Reliable cell-level annotations ... are scarce". In line 199 you write: "while having reliable GT ... at the instance (cell) level. Same as comment 5). This creates confusion.

11) In line 204 you cite some other datasets. Since your main contribution is the creation of PAP-QMNIST, it would enhance your case if you would show comparative performance of a classification method(s)based on your dataset and those you cited.

12) In line 215 you write "transformations" expected in OC. It would the reader if you described those, or if you cited a paper that does.

13) Have you tried any AI colorization scheme? There are some free tools for this as well. Would be interesting to see if they have any effect compared to your way of colorization the QMNIST images.

14) In lines 221-222 you write: "...give colorization visually far from colors of OC data". How do you check this? Please include it in paper.

15) In line 225 you write: "if color appears as an important feature... over-fitted the data". Couldn't be that color is an important feature in general and has predictive power? Have you compared the performance with greyscale images? The way you present it is that if color ends up being important then there is over-fitting, which makes one wonder why bother with it then?

16) You need to explain your claim in 236-239. The impact of the ratio could differ between different classification schemes.

17) Based on your claim in lines 254-258 wouldn't the digit 6 or 9 make a better candidate than 4?

18) In line 262 "positive labels" refer to images with digit 4 (though all the tranformations)?

19) In line 267 you write:"For each key instance setting". What are the different "settings"?

20) In line 21 you mention F1 score again. Please consider including other metrics like TPR, FRR, etc.

21) In line 295 you mention that you trained the model for 20 epochs. At first glance, this seems like a low number. Have you tried training for 50 or 100 epochs to see if the behavior changes?

22) In line 315 you mention crossover of precision and recall. Since this happens at the instance level wouldn't your data be skewed towards negative samples making precision unreliable? Why did you choose that?

23) In line 321, is sampling with replacement or without?

24) In lines 330-334, it is not clear why you impose extra conditions. An instance in bag i is considered to be correctly detected as key instance when i and ii and iii hold true. But ii writes that the true label is positive. Isn't that information alone sufficient??

25) In line 371 put the phrase "for each patient in the test set" at the beginning of the sentence.

26) In line 381, you write you do not compare human performance .... Why not? Wouldn't that be useful? Such a method could be used as a substitute in places with lack of experts.

27) The results section should be combined with the Discussion section to highlight its findings. Currently, it seems that it "aggregates" the captions of tables and figures without offering any insights on what they mean and what value they add to the story of the paper.

This paper would benefit if the authors made a more "concetrated" effort to highlight their contribution, which is the dataset they created and how it can be used to train systems able to detect OC. If they reduced/reorganized figures and tables, it would enable focusing on the dataset and networks they used, which in turn would highlight their findings.

Reviewer #2: Dear authors,

I read with great interest the manuscript, which falls within the aim of this Journal. In my honest opinion, the topic is interesting enough to attract the readers’ attention. Nevertheless, authors should clarify some points and improve the discussion, as suggested below. Authors should consider the following recommendations:

In my opinion you have to refer in the paper to the updated literature about how the technique can be usefull for oral cancer detection as well for ohter cancer endometrial cancer and in general for all malignanices .

Also in infertility as has been demonstrated how machine learning can be helpfull.

I suggest you to read and cite these articles:

Circulating miRNAs as a Tool for Early Diagnosis of Endometrial Cancer-Implications for the Fertility-Sparing Process: Clinical, Biological, and Legal Aspects

Fertility-Sparing Strategies for Early-Stage Endometrial Cancer: Stepping towards Precision Medicine Based on the Molecular Fingerprint

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks

Endometrial Cancer in Reproductive Age: Fertility-Sparing Approach and Reproductive Outcomes

Reviewer #3: This study seeks to explore AI-based methods for oral cancer (OC) detection, aiming to provide a less invasive alternative to histological examination. By comparing conventional single instance learning (SIL) and modern multiple instance learning (MIL) approaches using real OC data and a synthetic dataset (PAP-QMNIST), the study attempts to determine their effectiveness in identifying malignant and dysplastic cells. However, the study's scope is limited to performance evaluation and lacks a comprehensive analysis of the potential challenges and practical implications of implementing AI-driven OC detection in clinical settings. Additionally, while it compares SIL and MIL methods, it does not explore other AI-based approaches or address the need for interpretability and validation in real clinical scenarios, leaving room for further research in these critical areas.

Reviewer #4: 1) Avoid the extensive use of words like "we", "our"

2) Don't mention the URL of the code in abstract section

3) The introduction section is too long. It is recommended to summarize this section.

4) Figures and Tables captions are too long (in most cases)

5) The number of patients (24) is limited for a comparative study. Explain how could you overcome this limitation.

6) Why have you chosen certain measures of performance and neglected other measures? Justify

7) Give a comparison (in tabulated form) with previous studies in the field (before conclusion section)\\

8) Add (3-5) recent references 2022-2023

Reviewer #5: Please find the detailed comments as follows:

1. Authors have conducted experiments on a very limited and outdated set of deep learning models, which is insufficient in the current scenario.

2. Merely mentioning ResNet is not enough; it should clearly state which version of the ResNet model was used.

3. The manuscript lacks a state-of-the-art comparison with other models, such as DenseNet 201, EfficientNet-B0 to B7, MobileNet v3, etc.

4. There are very limited details about the deep learning models in the manuscript. Please provide more details.

5. Architectural diagrams of the deep learning model are also missing.

6. The details of the hyperparameters in the manuscript are not sufficient to support the reproducibility of the results. Just providing the GitHub link is not enough; crucial details should be in the main manuscript.

7. The manuscript does not properly state the research gaps that motivated the proposed work.

8. There are various grammatical and typographical mistakes in the manuscript. Please correct them.

9. A well-designed block diagram to illustrate the complete methodology would enhance the understanding of the manuscript.

10. The literature review is quite outdated and not very significant. Please include recent papers. Authors are also encouraged to discuss how the proposed work significantly contributes in the context of those recent works.

11. Authors should state the limitations of the proposed work and provide directions for future research.

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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: Yes: Giuseppe Gullo

Reviewer #3: No

Reviewer #4: Yes: Hossam El-Din Moustafa

Reviewer #5: Yes: Rakesh Chandra Joshi

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PONE-D-23-20689R1Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detectionPLOS ONE

Dear Dr. Koriakina,

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 Mar 25 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.

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,

Sathishkumar Veerappampalayam Easwaramoorthy

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.

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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 #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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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 #2: Partly

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: 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 #2: Yes

Reviewer #3: Yes

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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 #2: 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 #2: Dear authors,

I read with great interest the manuscript, which falls within the aim of this Journal. In my honest opinion, the topic is interesting enough to attract the readers’ attention. Nevertheless, authors should clarify some points and improve the discussion, as suggested below. Authors should consider the following recommendations:

In my opinion you have to improve the paper refering in the text how its really impoerant to refer and compare to the PAPILLOMAVIRUS and oral cancer correlation and how artifical ointelligence in this denatality ERA can be really importnat especially in pts with cervical and other malignanicues as endometrial cancer that need to preserv their fertility by oocite vitrification for future use before fertility sparing surgery treatment.

Tthe artifical intelligence contribution as well how is suggested to these pts to perform a NIPT TEST at beginning of pregnancy as well to follow up the impart of ASSITSTED REPRODUCTIVE TECHNOLOGY ART) in the newborn.

I SUGGEST YOU TO READ AND CITE THESE ARTICLES:

Open vs. closed vitrification system: which one is safer?

Endometrial Cancer in Reproductive Age: Fertility-Sparing Approach and Reproductive Outcomes

Neoadjuvant chemotherapy in advanced-stage ovarian cancer – state of the art

Fertility-Sparing Strategies for Early-Stage Endometrial Cancer: Stepping towards Precision Medicine Based on the Molecular Fingerprint

Neonatal Outcomes and Long-Term Follow-Up of Children Born from Frozen Embryo, a Narrative Review of Latest Research Findings

Circulating miRNAs as a Tool for Early Diagnosis of Endometrial Cancer-Implications for the Fertility-Sparing Process: Clinical, Biological, and Legal Aspects

Fresh vs. frozen embryo transfer in assisted reproductive techniques: a single center retrospective cohort study and ethical-legal implications

The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks

Cell-Free Fetal DNA and Non-Invasive Prenatal Diagnosis of Chromosomopathies and Pediatric Monogenic Diseases: A Critical Appraisal and Medicolegal Remarks

Impact of assisted reproduction techniques on the neuro-psycho-motor outcome of newborns: a critical appraisal

Sentinel Lymph Node Staging in Early-Stage Cervical Cancer: A Comprehensive Review

Reviewer #3: Accept

All comments have been addressed

I would like to thank the authors for their efforts

the conclusion is justified by the methods and the results

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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 #2: Yes: giuseppe gullo

Reviewer #3: 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.

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection

PONE-D-23-20689R2

Dear Dr. Koriakina,

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.

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Kind regards,

Sathishkumar Veerappampalayam Easwaramoorthy

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: