Venue: MIDL 2023 Short_Paper_Track

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper tackles tumor detection via a MIL-like weakly-supervised regression approach to predict the percentage of tumor present in WSIs, a clinically available target that allows to overcome the problem of need for manual annotations or presence of tumor-free slides. The experimental results were investigated on two datasets. Although it is interesting to see such a study on percentage prediction of tumors in WSI, how this facilitate the diagnosis and compare with existing studies with weakly supervised learning methods are missing. 
review_title: Tumor percentage as an explainable detector in histopathology whole-slide images

final_score: rating: 7: Good paper, accept
review_content: The authors introduce a weakly-supervised regression-based approach for estimating the percentage of tumor in a WSI. The clinical focus is justified reasonably. The paper is very well written paper with interesting results that will generate healthy discussions during the conference. 
review_title: good paper 

Decision: Accept(Poster)