Venue: MIDL 2022 Conference

Reviews:

final_score: rating: 4: Weak accept
review_content: summary: The paper describes a method for semantic segmentation in medical images. The proposed architecture is based on the U-Net and the previously proposed HistNet models. The authors modified the HistNet model by incorporating new skip pathways to the decoder part to aggregate multi-scale input features and a feature pyramid architecture. The proposed method was applied on two datasets and achieved superior performances compared to a number of deep learning-based approaches.
strengths: - The paper is well-written and easy to follow in most parts. 
- The proposed method achieved a supervisor performance compared to the baseline HistNet and a number of deep learning-based models. 
- The effectiveness of new proposed parts in the modified model is comprehensively investigated in an ablation study. 
weaknesses: - The technical novelty and contribution in comparison to the previously proposed HistNet model is limited 
- The effectiveness of the proposed deep supervision in the proposed workflow is questionable  
detailed_comments: Please refer to the "Questions To Address In The Rebuttal"  and the "Weaknesses" sections.
recommendation: Poster
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: This work addresses the semantic segmentation task applied to digital histopathology imaging. The authors propose several architectural changes departing from their previous work and leading to improved performances and lighter memory footprint (i.e., less trainable parameters). The evaluation is performed on the PANDA and DigestPath2019 datasets.
strengths: The authors propose a modified u-net architecture allowing for higher performances and a lower number of parameters, which is appealing. The authors report a substantial amount of results, mainly on PANDA dataset, but also on DigestPath2019, which outlines the interest of the proposed method.

weaknesses: The proposed method is composed of multiple components and strategies, namely long skip pathways, multi-scale input feature map, and deep supervision training approach. This makes less obvious the understanding of the main contribution of the paper.
recommendation: Poster
review_title: null

final_score: rating: 3: Borderline
review_content: summary: The paper proposes a segmentation approach aimed at histopathology applications. It is an extension of the previous work of the author by multi-scaled feature maps (extracted from downscaled versions of the input image). Additionally, the model architecture was fine-tuned by using an auxiliary loss at lower resolution. 

strengths: - The idea of incorporating multi-level features is interesting and already well established in histopathology. 

- The paper combines some of the more recent ideas for segmentation models and evaluates them in a concise way.

- The model architectural decision all seem sound and the evaluation contains an ablation study.

weaknesses: The main weakness of this paper is that it was really hard to understand. This stems in part from the fact that it is strongly related to previous work of the same authors (HistNet), which represents an optimized version of a segmentation model aimed for histopathology. 

To the reviewer it is not clear if the many modifications made against SOTA models are an overfit towards properties of the data sets or would be robust also for other data sets. Yet, the relatively small increments in performance given the large standard deviation in the ablation study could hint towards this. 

As per table 1 of the original HistNet paper, it seems that the original architecture was tuned on the DigestPath2019 challenge dataset (test set). This somewhat restricts the possibilities to evaluate and compare against other methods where this was not done (could be perceived as a hyperparameter search on the test set). I have the same impression for this paper and the PANDA dataset (it’s unknown if the design choices were made on the validation set or on the test set, but the latter is my impression from reading the paper).  

One of the data sets (the PANDA 2020 data set) is not yet released for own publications. The text on the website states:
"We ask challenge participants / researchers to respect an embargo until the challenge paper has been published. " (https://panda.grand-challenge.org/data/) Further, it is not clear if the evaluation was carried out on the test set of PANDA 2020 or the training set (and if, how this set was generated), or one of the sets available for evaluation on the kaggle platform. 
detailed_comments: - Some parts of the introduction are verbatim copies or almost verbatim copies from the earlier paper of the authors. Under some regulations this is considered self-plagiarism. Please be cautious when copying from your old papers.

- I honestly don’t have a picture of your complete model in mind after reading the paper. In Fig. 1 the encoder block seem to be skipped. But how do you generate the downscaled multi scale input feature maps? I also don't understand how you can combine blocks of different feature resolution (yellow/blue/green/purple) without an upconvolution / convolution (which I don't find in Fig. 1). 

- Please have in mind that the reader of your paper will typically not have read the HistNet paper beforehand. It would really help your impact if the paper would be understandable on its own without having to read your previous work. 
recommendation: Poster
review_title: null

Meta-review: metareview: The paper presents an improved version of an existing model from the same group (HistNet), and shows some improvement across several datasets. Despite the fairly incremental contribution of this paper, reviewers generally pointed out that it has some value and that it might be interesting for the MIDL community.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)