Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study investigates the impact of Color Normalization (CN) techniques on the performance of Content-Based Histopathological Image Retrieval (CBHIR) tools. The research focuses on whether applying CN as a preprocessing step can improve the accuracy and effectiveness of CBHIR in diagnosing cancer from Whole Slide Images (WSIs).

·       The authors should provide more detailed statistical analysis to strengthen the evidence for the effectiveness of the chosen CN methods.

·       The tables and figures effectively illustrate the research findings. However, including more visual comparisons between the different CN techniques could enhance understanding.

·       The language of the manuscript should be revised for clarity and conciseness. The use of bullet points should be avoided in favor of more formal academic writing to enhance the professionalism and readability of the text.

·       A discussion on the broader implications of the findings and how they contribute to the field of digital pathology is needed. This should include potential future research directions, how these CN techniques might be integrated into current diagnostic workflows, and their impact on the accuracy and efficiency of cancer diagnoses.

·       The discussion should also delve into the technical considerations of implementing these CN techniques in real-world settings. This includes discussing the feasibility, resource requirements, and any potential obstacles to integrating these techniques into existing medical image analysis systems.

Comments on the Quality of English Language

Extensive editing of English language required

Author Response

Dear Reviewer

Thanks for reviewing our manuscript and for your insightful comments. We tried to cover all your concerns to improve our paper.

 

Best

Zahra

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript presents the CBHIR framework for histopathological images based on an unsupervised feature extractor and color normalization. Experimental results on the CAMELYON17 dataset are effective, especially the CN technique, with a positive impact on the retrieval performance in the proposed CBHIR framework. Some concerns from my side are given as follows. 

1. Three CNs used in the Pre-processing section should be described in detail.

2. In section 3.2, a complete and detailed framework diagram of CAE should given.

3. In section 3.3, do query features get one set of features per image or fixed N sets for all Query images?

4. Some of the latest related references need to be discussed or compared, such as:

Multi-focus image fusion based on fractional order differentiation and closed image matting, ISA Trans. Injected Infrared and Visible Image Fusion via L1 Decomposition Model and Guided Filtering, IEEE TCI.

Comments on the Quality of English Language

It can be improved.

Author Response

Dear Reviewer

Thanks for reviewing our manuscript and for your insightful comments. We tried to cover all your concerns to improve our paper.

 

Best

Zahra

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Excellent article, well written with proper scientific structure that will bring some novelty to the field. Potential solution for the bottleneck problem in feature extraction algorithms. Validated in well-known breast cancer pathology images. Clear aims answered by the experiment and results. Proficient English, good images, and graphs. I recommend for publication if authors address following minor issues:

·      Add relevant citations to text in lines: 22-23, 185-191. Those ideas did not sound out from the paper results, and relevant sources should be cited. 

·      Please compare authors’ algorithm to the physician – pathologist, real- world performance. You do not need to do extra experiments. Cite relevant sources and add 3-5 sentences to the results section eg. lines 285 – 292 or/and add relevant column to the Table 2. 

Great paper, congratulations!

Author Response

Dear Reviewer

Thanks for reviewing our manuscript and for your insightful comments. We tried to cover all your concerns to improve our paper.

 

Best

Zahra

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for submitting the revised version of your manuscript.

To proceed with the next stage of the review process, I kindly request that you provide a A detailed, point-by-point response to each of the concerns I raised in my initial review. For each point, please explain how you have addressed the issue and where in the revised manuscript these changes can be found (including specific line numbers or sections).

If possible, please include a version of the manuscript that clearly shows the changes made (e.g., using track changes or highlighted text). This will facilitate a quicker and more efficient assessment of the revisions.

 

 

Comments on the Quality of English Language

extensive revision 

Author Response

Dear Reviewer

We express our gratitude to you for your time and effort in critically reviewing our manuscript.

In the attachment, we replied to your comments one by one and mentioned the lines and section number of each change that we made.

Thanks for your time and consideration.

Best

Zahra

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Authors answered all coments included in the previous report. In the current form I do not see any flaws. Suitable for publication. 

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

I recommend the publication of this article. 

Comments on the Quality of English Language

extensive revision