Venue: MIDL 2023 Conference

Reviews:

final_score: rating: 3: Borderline
review_content: summary: The paper proposes a case-based interpretable deep learning model for brain tumor classification with 3D multi-parametric magnetic resonance imaging (mpMRI) data. The model is called MProtoNet and is an extension of ProtoPNet, which is a case-based reasoning model for image classification. More specifically, the authors added an online-CAM loss and a soft masking to XProtoNet, and replaced 2D filters with 3D. Overall, the paper is of interest, but some questions need to be addressed. 
strengths: - Interpretable models in medical imaging are an ongoing research topic and of high interest.
- Use of soft masking to refine attention maps from XProtoNet
- Online-CAM loss function that uses image-level labels during image analysis.
weaknesses: - I find the paper unfocused on the main contribution and rather re-explaining what was already done in XProtoNet.
- The paper does not mention the computational cost of the proposed method and how it compares to other state-of-the-art methods in terms of speed and memory usage. 
detailed_comments: - I suggest that the authors re-write the paper putting the main contribution forward, and refer to XProtoNet when needed. Avoid using sentences such as "L1 was introduced, we introduce focal loss"...etc, these were introduced in XProtoNet and may be confusing to the reader.
- I suggest to re-write the sentences such as " Given that XProtoNet is most similar to MProtoNet" to "Given that MProtoNet is most similar to XProtoNet " ... throughout the whole paper. XProtoNet is the original architecture and MProtoNet is a variant with an online-CAM loss. 
- I suggest to focus on the differences between MProtoNet and XProtoNet, for the rest of the description.
- Please move the appendix to the main text, this part is the main work that has been done  and important to the reader.
- Related  work could be extended. Several papers for interpretable deep learning models using class-label to guide attention were proposed for natural images and medical imaging, for instance:
1- Zhou, Y., Zhu, Y., Ye, Q., Qiu, Q. and Jiao, J., 2018. Weakly supervised instance segmentation using class peak response. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3791-3800).
2- Amyar, A., Modzelewski, R., Vera, P., Morard, V. and Ruan, S., 2022. Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction. Journal of Imaging, 8(5), p.130.

recommendation: []
review_title: null

final_score: rating: 5: Strong accept
review_content: summary: The authors present a case-based interpretable model for medical applications and evaluate their proposed method on brain tumor classification (high grade vs. low grade). The method is based on ProtoNet. The evaluation shows a statistically significant superior classification performance and improved interpretability.
strengths: The paper is clearly motivated and discusses prior work, research gaps, and specific contributions. The writing is clear, and the figures and tables help the reader to understand the experiments and the results. The experimental setup is described clearly and includes an ablation study and a reasonable baseline.
weaknesses: The authors use a five-fold cross-validation for each method they compare. The fine-tuning and parameter setting (and potentially the loss-weighting?) should, in my opinion, be limited to a single split of this five-fold CV, which seems not to be the case.
detailed_comments: I appreciate the very detailed experimental setup description, making the work reproducible. The appendix provides additional information to also visually appreciate the localization coherence of the different models.
recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: This manuscript discusses the use of deep convolutional neural networks (CNNs) in medical imaging and the challenges of interpretability. The authors propose a new model called MProtoNet, which is an extension of ProtoPNet for use in multi-modal 3D medical images, specifically brain tumor classification with 3D multi-parametric magnetic resonance imaging (mpMRI) data. MProtoNet includes a new attention module with soft masking and online-CAM loss that helps to sharpen attention maps and directly utilizes image-level labels during training. The results show that MProtoNet achieves statistically significant improvements in interpretability metrics compared to baseline CNNs and other ProtoPNet variants.
strengths: -MProtoNet achieves the best performance in terms of both correctness and localization coherence, excelling in interpretability.

-The attention module with soft masking and online CAM loss is a novel solution to the differences between 2D natural images and 3D mpMRIs.

-Results show that MProtoNet achieves statistically significant improvements in interpretability metrics of both correctness and localization coherence without any annotated labels during training when compared with baseline CNN and several ProtoPNet variants.

weaknesses: -It is not clear from the abstract how well MProtoNet performs in comparison to other existing approaches for interpretability in medical imaging.

-The authors should provide a few more examples.

-As mentioned by the authors, it's possible that incorporating other interpretable methods, such as concept-based models, could enhance MProtoNet's interpretability in more complex medical image applications.

recommendation: Oral
review_title: null

Meta-review: metareview: This an excellent and well-written study, with interest to the community. It addresses an important problem of interpretablity in medical imaging. I recommend it for an oral presentation.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)