Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper claims that neural networks can be trained on histopathology images with incomplete annotation for object detection without large sacrifice on accuracy. The hyperparameter configuration proposed minimizes the accuracy loss efficiently without modification on the architecture or the training pipeline. 
strengths_and_weaknesses: Thank you for submitting this work to Tiny Papers @ ICLR 2023. The paper targets an important problem and presents an effective and efficient solution to deal with it. 

Strength
- The paper describes the importance of the problem and the methodology clearly. The problem it focuses on is realistic and important, especially in the histopathology domain, where the lack of image annotations is even more substantial. It also includes and discusses previous relevant literature. 
- The results of the experiment successfully prove their hypothesis that neural network can be trained efficiently in the absence of annotations without significant modifications. 
- The paper follows the basic formatting requirements, page limit, as well as the ICLR code of conduct.

Weakness
- The model and hyperparameter settings are clearly described, but it would be better if the paper provides the code they used to reproduce the findings.
- The authors mention that the hyperparameters are tuned manually. However, in addition to the "tuned values" and "default values" listed in Table 2, more values of hyperparameters and the corresponding results are encouraged to be provided. Is the combination of hyperparameters specific to the model/dataset (i.e., can it be generalized to other models/datasets)? Is it possible to automate the tuning process to make the method more generalizable?
suggested_changes: The motivation to make modifications on hyperparameter configuration but not on the architecture or the training pipleline is interesting and practical. Here are some suggestions to further improve the paper.
- Apply the hyperparameter setting to larger/more diverse datasets to test its effecitveness
- Automate the hyperparameter searching process with grid search, random search, or other optimization algorithms with a larger hyperparameter space
- For future directions, try to annotate different portions of the images to see if it will make a difference on the accuracy
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This paper experimentally shows that incomplete annotation in object detection on histopathology datasets has marginal effects on the accuracy. Moreover, tuning the model can further decrease the performance gap. 
strengths_and_weaknesses: Strengths:
- The paper is well-written and easy to read.
- The authors provide sufficient details of the experiments and the method.
- The contribution of the paper seems very helpful for the domain experts since with around half the annotations, the results are almost unchanged.
- The suggested tuning set-up notably improves the performance on incomplete annotations.

Weaknesses:
- The experiments are done on only one dataset. It would be more insightful to see whether the argument holds for various datasets as well.
suggested_changes: I would suggest the authors investigate their argument also on segmentation tasks for their future works. Segmentation is another time-consuming task for experts.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This work showed that a well-tuned YOLOv5 can learn effective to detect objects from histopathology datasets with limited annotations. The well-tuned model can perform substantially well even when only 10\% objects are annotated.
strengths_and_weaknesses: Strengths
- Limited training samples in object detection is an important problem, which manifests in many real-world application.
- The paper is generally well-written and is likely to be reproducible.
- The experimental results are encouraging.

Weaknesses
- It is unclear to me where the improvement came from: is it from the better hyper-parameter tuning, or from some inherent properties of YOLOv5? 
- The authors are strongly encouraged to discuss more on the foundation, results, and extendibility of this work: can better hyper-parameters search translate to other architectures such as DETR and to other datasets; what are the principles to address the challenge of incomplete object annotation; why did the new set of hyper-parameter improved the results?

suggested_changes: See Weakness
review_title: null

Meta-review: metareview: This paper shows that the limited annotations in object detection for histopathology has little effect on the detection accuracy using a fine-tuned YOLOv5. The problem is important and the results are encouraging. I believe this paper is interesting, clear, and correct, with some pros and cons listed below.

Pros:
1. The studied problem is meaningful and important -- the lack of image annotations is common in real-world applications such as the histopathology domain;
2. The reported results are promising and has the potential to be impactful in the histopathology domain;
3. The paper is well-written and easy to understand, and methodology details are sufficient.

Cons:
1. The authors are suggested to try some other datasets and see similar results can be found;
2. The authors are encouraged to study where the promising results came from, for example, they are from fine-tuned parameters or inherent property of YOLOv5, or some others;
3. The authors are encouraged to share the code for reproducibility.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to present