Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening
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SciScore for 10.1101/2020.09.14.20194654: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There exist a few limitations with regard to the proposed method. Though attention maps could show interpretability and explainability for COVID-19 diagnosis, there exist some failure cases where the attention map do not correctly indicate an infected region as shown inFigure 5. Second, we found that extensive data augmentation such as …
SciScore for 10.1101/2020.09.14.20194654: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There exist a few limitations with regard to the proposed method. Though attention maps could show interpretability and explainability for COVID-19 diagnosis, there exist some failure cases where the attention map do not correctly indicate an infected region as shown inFigure 5. Second, we found that extensive data augmentation such as color jittering lead to reduced performance and was largely negligible compared to the benefit of using a contrastive loss which showed consistent improvements across all evaluation settings. This motivates us to consider using more complex attention modes for better diagnostic interpretability as well as explore unsupervised pre-training using the proposed method both in 2D or 3D as future directions.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 12. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a protocol registration statement.
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