Venue: MIDL 2022 Conference

Reviews:

final_score: rating: 5: Strong accept
review_content: summary: The author has focused on the blood serum biomarkers BNP and BNPP, indicative of acute heart failure (HF) and cardiogenic pulmonary
edema, paired with the chest X-ray imaging modality. They investigated the potential for inferring BNP and BNPP from chest radiographs. 
This is quite interesting work in medical science. 
strengths: This paper has well written. Each segment of this paper has been explained very well and references are organized very perfectly.  
The author has used Train, Validation, and Test which is a very good way to validate the results. 
weaknesses: Since this conference is only for medical imaging and machine learning that is more into engineering conferences. I had one concern because this paper has been written in a more engineering way and clinicians will not understand much.
detailed_comments: No comments
recommendation: Best Paper Award
review_title: null

Meta-review: metareview: The authors proposed an approach to assist the diagnosis of pulmonary oedema from X-rays by training CNNs to predict blood serum biomarkers instead of manually defined labels. The authors target what appears to be an important clinical application. The paper is clearly written. Even though not very innovative the method seems sound. The evaluation is thorough and original, and relies on a large data set.
The authors took the opportunity of the rebuttal phase to clarify a few points, both by replying to the reviewers and by uploading a revised manuscript.

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Minor detail: the caption of Table 3 in the appendices should be corrected as it does not correspond to the content of the table.

confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)