Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsAbstract should be updated to include some numerical results.
Add one paragraph to describe the structure of the paper by the end of section 1.
In section 2, the authors are advised to enrich the discussion of the important literature works in deep learning (“Mohamed, M. (2023) “Empowering deep learning based organizational decision making: A Survey”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33105.”, “Mohamed, M. (2023) “Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories”, Sustainable Machine Intelligence Journal, 4, p. (2):1−20. doi: 10.61185/SMIJ.2023.44102.”) through discussing them in introduction/literature or include them within experimental comparisons.
in section 4.3, evaluation metrics miss math expressions.
it's highly recommended to use cross-validation in you experiments.
Experimental comparisons miss statistical analysis
Comments on the Quality of English Language
The authors should strive for greater clarity and precision in their use of the English language.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe authors have enhanced the SAM (Segment Everything Model), a recently published model, by incorporating predicted masks to facilitate histopathology image segmentation. While the innovation in the method appears somewhat limited, its adaptability to various image types is acknowledged. I recommend including the original SAM as one compression method to augment the paper, considering that SAM may have been initially trained using the Lizard dataset. This addition would provide valuable context and insight into the model's evolution, enhancing the study's comprehensiveness.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors
- The limitations should be pointed out before conclusion
- The authors are advised to address generalizability of the proposed method
Comments on the Quality of English Languagegood
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
