Title: AI Support for Colonoscopy Quality Control Using CNN and Transformer Architectures
Version: 1 Date: 18 Jul 2024
Reviewer's report:
The authors of this paper propose a deep learning method for colonoscopy quality control using
various architectures. Additionally, the authors present methods to explore the black-box nature
of the proposed model for better decision-making. Essentially, this study introduces a method
to detect the cleanliness of the colon given certain frames.
However, the technical novelty of this study is very weak. Using an existing model such as
EfficientNet does not constitute novelty unless significant improvements or changes have been
made to its architecture. I recommend that the authors focus more on the novelty of their method,
specifically detailing the new knowledge or advancements created by this study.
Moreover, based on the results, it seems that the model does not perform very well on the
external dataset, with precision and recall dropping to 0.948 and 0.952, respectively. The authors
should add a discussion on this change in metrics to provide more insight into the performance
discrepancies.
The objective of the study is also quite confusing. Unless the authors are introducing a
comprehensive system to detect polyps as well as assess bowel cleanliness, they should focus
on either one of these two objectives.
Similarly, in Figure 3, it is noted that the softmax function maps to polyps and the cecum,
in addition to two classes related to bowel cleanliness. This is quite confusing and needs
clarification.
Figure 4 does not look professional. Please revise the icons and the flow to enhance the visual
presentation.
The use of TensorFlow, Keras, and PyTorch together is unusual. The authors should provide a
logical explanation for this decision.
Figure 5 does not provide any useful information. Consider using a different type of visualization
to convey the necessary information more effectively.
Given these concerns, I do not recommend this paper for publication at this moment.
Title: AI Support for Colonoscopy Quality Control Using CNN and Transformer Architectures
Version: 1 Date: 18 Jul 2024
Reviewer's report:
The authors of this paper propose a deep learning method for colonoscopy quality control using
various architectures. Additionally, the authors present methods to explore the black-box nature
of the proposed model for better decision-making. Essentially, this study introduces a method
to detect the cleanliness of the colon given certain frames.
However, the technical novelty of this study is very weak. Using an existing model such as
EfficientNet does not constitute novelty unless significant improvements or changes have been
made to its architecture. I recommend that the authors focus more on the novelty of their method,
specifically detailing the new knowledge or advancements created by this study.
Moreover, based on the results, it seems that the model does not perform very well on the
external dataset, with precision and recall dropping to 0.948 and 0.952, respectively. The authors
should add a discussion on this change in metrics to provide more insight into the performance
discrepancies.
The objective of the study is also quite confusing. Unless the authors are introducing a
comprehensive system to detect polyps as well as assess bowel cleanliness, they should focus
on either one of these two objectives.
Similarly, in Figure 3, it is noted that the softmax function maps to polyps and the cecum,
in addition to two classes related to bowel cleanliness. This is quite confusing and needs
clarification.
Figure 4 does not look professional. Please revise the icons and the flow to enhance the visual
presentation.
The use of TensorFlow, Keras, and PyTorch together is unusual. The authors should provide a
logical explanation for this decision.
Figure 5 does not provide any useful information. Consider using a different type of visualization
to convey the necessary information more effectively.
Given these concerns, I do not recommend this paper for publication at this moment.
Please review this paper.
user
Title: AI Support for Colonoscopy Quality Control Using CNN and Transformer Architectures
Version: 1 Date: 18 Jul 2024
Reviewer's report:
The authors of this paper propose a deep learning method for colonoscopy quality control using
various architectures. Additionally, the authors present methods to explore the black-box nature
of the proposed model for better decision-making. Essentially, this study introduces a method
to detect the cleanliness of the colon given certain frames.
However, the technical novelty of this study is very weak. Using an existing model such as
EfficientNet does not constitute novelty unless significant improvements or changes have been
made to its architecture. I recommend that the authors focus more on the novelty of their method,
specifically detailing the new knowledge or advancements created by this study.
Moreover, based on the results, it seems that the model does not perform very well on the
external dataset, with precision and recall dropping to 0.948 and 0.952, respectively. The authors
should add a discussion on this change in metrics to provide more insight into the performance
discrepancies.
The objective of the study is also quite confusing. Unless the authors are introducing a
comprehensive system to detect polyps as well as assess bowel cleanliness, they should focus
on either one of these two objectives.
Similarly, in Figure 3, it is noted that the softmax function maps to polyps and the cecum,
in addition to two classes related to bowel cleanliness. This is quite confusing and needs
clarification.
Figure 4 does not look professional. Please revise the icons and the flow to enhance the visual
presentation.
The use of TensorFlow, Keras, and PyTorch together is unusual. The authors should provide a
logical explanation for this decision.
Figure 5 does not provide any useful information. Consider using a different type of visualization
to convey the necessary information more effectively.
Given these concerns, I do not recommend this paper for publication at this moment.
assistant