Venue: MIDL 2023 Conference

Reviews:

final_score: rating: 3: Borderline
review_content: summary: This paper achieves the tasks of COVID-19 pneumonia detection, feature extraction, classification and recognition synchronously. Experimental results show that the proposed model has better classification performance and diagnosis effect than previous methods, with great clinical application potential. On the whole, putting multi-task on COVID19 is strong for the problem, but the motivation of the structural design is insufficient, such as splicing some deep learning models.
strengths: 1) This framework mainly composed of object detection, depth feature generation, and multi-channel fusion classification, which plays a role in clinical application potential.
2) Experimental results show that the proposed model has better classification performance and diagnosis effect than previous methods
weaknesses: 1) The title and the structure of framework feels like connecting multiple deep learning models in series, making it difficult to grasp the focus and contribution.
2) The motivation of introducing the MLP-Mixer is insufficient.
3) Operational efficiency compared to multi-stage cascaded models is not supported by experiments
detailed_comments: 1) Is there anything special about generating 5-dimensional (5D) feature maps, and what about generating feature maps of larger or smaller dimensions?
2) More motivations of introducing the MLP-Mixer need to be explained.
recommendation: []
review_title: null

final_score: rating: 5: Strong accept
review_content: summary: To fully extract the feature information of lung parenchyma in Chest X-ray images and realize the auxiliary diagnosis of Covid19 pneumonia, this paper proposed an end-to-end deep learning model, mainly composed of object detection, depth feature generation, and multi-channel fusion classification. Experimental results show that the proposed model has better classification performance and diagnosis effect than previous methods, with great clinical application potential.


strengths: -	The proposed end-to-end deep learning model can achieve the tasks of object detection, feature extraction, classification, and recognition synchronously.
-	By embedding an RPN-based object detection module in the end-to-end model, it realizes the precise positioning of the chest region. It reduces the interference of other regions and the calculation of subsequent features.
-	The convolution module with different receptive fields and the multi-scale MLP module achieve local and global information extraction. 
-	High-precision classification 

weaknesses: -	The Covid19 images have been augmented just by random rotation. 
- The text in the figures is too little to be readable.
- The graph in figure 2 is rotated 90 degrees, making it difficult to visualize it. 

recommendation: Best Paper Award
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: This paper proposes a new model for diagnosing COVID-19 pneumonia from chest X-ray images. The model consists of three main parts: object detection, feature generation, and multi-channel classification. 
Object detection through RPN intends to isolate the chest cavity, then the feature generation section creates 5-dimensional features from such region. Each feature map inputs into a three layers MLP pyramid which intends to extract multi-channel and multi-scale features that are transformed into a new set of compact features based on the correlations given by a Bi-LSTM module. Finally, the features are employed to classify the image as normal, COVID-19 or other pathology.  
strengths: - The model follows a nice engineering approach combining different usual "pieces" in state-of-the-art processing pipelines. This requires a great effort of implementation and adaptation to the resources usually available.
- The model stages are clear which facilitates a similar implementation.
- The results are promising and therefore make it worth trying the model in new datasets.
- The same approach could be easily adapted for other CXR task-related.
weaknesses: - Overall, the text seems to be written hastily and can be confusing at times. For example, the authors have not filled in the "Short Title" field.
-  The explanation of the stages of the model is simple to understand, but the important part, the implementation, is missing. There are also missing details about the model's design, which makes it hard to recreate even if the stages are clear.
- A big concern for me is the lack of validation, especially in a such problem as COVID detection. There are numerous reputable datasets available to the public$^{1,2}$.  The authors' choice to only analyze a single dataset with such a limited number of real positive cases raises questions.

- If object detection proves to be useful, could it be due to the well-known problem of spurious correlations$^3$ in this type of dataset instead of the influence of other tissues as the authors claim?
- Authors claim multi-tasking but just one task is coped disease classification.




[1] Costa et al. "COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers". Sensors. 2022

[2] Kaggle. "COVID-19 Radiography Database". 2021


[3] De grave et al. "AI for radiographic COVID-19 detection selects shortcuts over signal". Nature Machine intelligence. 2021
detailed_comments: Given the number of reviews and the sort of rebuttal period, I prefer to avoid minor and detailed comments.  I hope that both the authors and the AC are in agreement with this. If you need clarification on any point, please let me know.
recommendation: []
review_title: null

Meta-review: metareview: Overall, all reviewers are satisfied with the response given by the authors, and are glad to see that the quality of the paper has been improved substantially.
confidence: 5: The area chair is absolutely certain
Decision: Accept (Poster)