Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: In this paper, the authors focus on the problem of oral lesion recognition. To this end, this paper proposes a general method to incorporate expert knowledge during training to reduce the number of implausible predictions based on anatomical location.
strengths_and_weaknesses: **Strength**
* S1: The paper presents results which show that the method improved performance as compared to the baseline
* S2: The appendix clearly explain each metric and presents additional results to support the claims made in the paper
* S3: The paper clearly shows the hyperparameters used to train the model.
* S4: The data collection methods have been explained clearly

**Weaknesses**
* W1: The paper tries different values of the parameter λ. However, for some values, the number of implausible preds is the same (for example λ = 10 and λ = 50). The discussion of results does not comment on this behavior.
* W2: The reason for using MCDropConnect has not been clearly explained. Moreover, the paper does not address why the loss function is suitable for use with MCDropConnect 

suggested_changes: * SC1: Brief description should be added in the *Discussion of Results* section to explain the effect of changing λ on the metrics.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The paper presents a novel training scheme to improve the performance of oral lesion recognition by imposing expert prior knowledge to a total loss while training neural network models. The proposed prior is a mean squared error (MSE) loss between predictions and ground truth of lesion types. Since the proposed regularized loss constraints to the lesion types and locations, the proposed scheme achieves better scaled brier scores and lower implausible predictions.
strengths_and_weaknesses: Strengths:
1. The paper was compressive and well-written. It contains unambiguous writing via separate sections having the introduction, approach, and experimentation. It is easy for readers to understand the problem that authors try to solve and what their contribution is.
2. The authors propose a simple but effective regularization loss that imposes expert prior knowledge to constrain the total loss for model training. Since the regularization loss penalizes the difference between predictions and ground truth of implausible lesion types, the proposed scheme achieves better Mutual Information and lower Implausible predictions.

Weaknesses:
1. The connection between the proposed regularization loss and Monte Carlo DropConnect (MC-DropConnect) is vague. The MC-DropConnect technique has been used to enhance model calibration in deep neural networks. The paper only mentions their proposed MSE loss but does not address the connection between the proposed loss and MC-DropConnect. Do the authors also consider MC-DropConnect while proposing the MSE loss? Or do the authors propose the MSE loss inspired by MC-DropConnect?
2. The evaluation metrics are not clear. The paper reports multiple measurement metrics for experiment comparisons. Since the proposed loss does not have a strong connection with MC-DropConnect, what do the reasons include Excepted Calibration Error in their experiment?
suggested_changes: Suggested Changes:
1. The paper can have descriptions for the connection between MC-Dropconnect and the proposed MSE loss to increase paper readability in the method section.
2. The authors can also also consider other evaluation metrics or explain why they choose the existing metrics presented in the paper.
review_title: null

Meta-review: metareview: The paper proposes a method for incorporating expert knowledge during training to reduce the number of implausible predictions based on anatomical location. The method imposes a mean squared error loss between predictions and ground truth of lesion types as a regularization loss, which constraints the lesion type and location. The proposed scheme achieves better-scaled brier scores and lower implausible predictions compared to the baseline. The paper is well-written and presents a comprehensive approach to the problem. However, the reviewers suggest improvements in the connection between the proposed regularization loss and Monte Carlo DropConnect, and the evaluation metrics.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present