Kati8/ICLR_2023_2025 huggingface dataset:
authors: Saeed Iqbal Khattak
authors: Adnan Nabeel Qureshi
authors: khalid javeed
authors: Khursheed Aurangzeb
reviews: 
Comments: This paper delves into an array of uncertainty quantification techniques with the aim of enhancing the amalgamation of predictions from multiple base learners. The authors critically examine the influence of noise and spatial transformations on image-centric tasks and introduce a test-time augmentation (TTA) methodology tailored for uncertainty. Consequently, predictions stemming from this approach manifest heightened accuracy. The proposed entropy-based uncertainty estimate (EUA), when synergized with TTA, outperforms established benchmarks, setting a new baseline in the domain.
Strengths: 1. This paper investigates the critical challenge of quantifying uncertainty in medical image segmentation powered by CNNs. The authors undertake a comprehensive exploration of potential solutions, ranging from uncertainty-sensitive fusion strategies to entropy-based uncertainty evaluation techniques. The conceptual foundation is robust, and the approach is astutely anchored in established prior works.

2. The introduced test-time augmentation, when amalgamated with EUA, consistently outshines several benchmarks. This accomplishment not only sets a commendable precedent but also furnishes valuable insights that can pave the way for subsequent research.
Weaknesses: 1. It would be helpful to clarify what “modality” refers to in this paper. Most recent studies in the medical domain consider clinical notes, vital signs, tabular data, and medical images as multimodalities. However, this paper focuses solely on CNN-driven medical image segmentation.

 2. The authors should discuss prior works on test time augmentation more thoroughly, for instance, the study found at https://www.nature.com/articles/s41598-020-61808-3. It’s also essential to highlight how the proposed TTA method improves upon previous approaches.

 3. The structure of this paper could be improved. For example, readers must refer to the appendix to access the experiment section, and there are numerous typos. The overall layout does not adhere closely to high-quality writing standards. Therefore, I recommend that the authors significantly revise the current version before submission.
Recommendation: 3: reject, not good enough
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 2 fair