REVIEW 4 major objections 6 minor 1 cited by
GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation
T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash
Pith's one-line read GloFinder packages automated glomerular detection as a single-click QuPath plugin and claims that ensembling five CircleNet models with Weighted Circle Fusion improves whole-slide detection accuracy over any single model.
desk verdict A genuinely useful QuPath plugin for glomerular detection, but the validation section overreaches: the WCF advantage may be threshold tuning on the test set, and the paper needs a proper validation split before the performance claims are publishable. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the circle representation: CircleNet predicts each glomerulus as a circle (center coordinates plus radius), a rotation-consistent representation suited to circular objects. Weighted Circle Fusion (WCF) is the ensemble mechanism that aggregates overlapping circles from multiple CircleNet models by weighting and merging based on confidence scores, using count and score thresholds to suppress low-consensus detections. The plugin's pipeline tiles the WSI into half-overlapping patches, runs five models, transforms coordinates back to whole-slide space, applies NMS, then WCF, and displays fusion results with colors indicating how many models agreed.
What would settle it
Select fusion thresholds on a distinct validation set, then measure $mAP(0.5{:}0.95)$ on an untouched test set; if the ensembled detector no longer beats the best single CircleNet model, the reported advantage is an artifact of threshold selection.
Extended reading notes
Core claim
The paper discovers that fusing the circle predictions of five independently trained CircleNet models, each trained on more than 160,000 manually annotated mouse glomeruli, yields a detector that outperforms every individual model and other fusion baselines such as NMS and Soft-NMS on an independent test set of 15 PAS-stained WSIs. The WCF ensemble weights overlapping detected circles by confidence and requires consensus from at least two models (T count = 2) with a high score threshold (T score = 0.9). When deployed inside the QuPath interface with color-coded visualization of consensus, GloFinder achieves the reported accuracy while cutting annotation time by roughly two-thirds in a human-in-the-loop setup.
Load-bearing premise
The fusion thresholds were not validated on a separate set; they were 'carefully chosen' and applied to the same 15 WSIs used for evaluation, so the measured gain over single models might partly reflect threshold tuning rather than a general property of the fusion method.
Editorial extensions
If this is right
- If GloFinder's claims are correct, non-programmer pathologists can perform WSI-level glomerular detection in QuPath at 21 seconds per slide.
- Color-coded consensus visualization lets clinicians prioritize review of low-consensus circles, which are more likely to be errors.
- The same plugin architecture can be retargeted to other circular biomedical objects, such as cell nuclei, by swapping the trained model.
- Human-in-the-loop annotation becomes fast enough to support large-scale curation of training data for future models.
- Ensembling models trained on diverse datasets through WCF may generalize better to unseen whole-slide images than any single model.
Reading between the lines
- The reported 0.829 mAP likely depends on the fusion thresholds (T count = 2, T score = 0.9) being tuned on the same test set; a held-out validation split would reveal whether the ensemble advantage persists.
- A testable extension is to evaluate WCF on other organ systems with circular objects and measure whether the consensus rule reduces annotation effort proportionally.
- Cloud-based execution could remove the local GPU requirement that currently limits accessibility for under-resourced labs.
- The 21-second per-slide runtime suggests the bottleneck for clinical adoption is not detection speed but integration with existing pathology information systems.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper presents GloFinder, a QuPath plugin for whole-slide-image (WSI) glomerular detection that runs five CircleNet models in the background and combines their outputs using a proposed Weighted Circle Fusion (WCF) ensemble. The plugin is designed for single-click operation in QuPath, with graphical visualization and editing of detected circles. The authors report that WCF achieves mAP(0.5:0.95)=0.829 on 15 PAS-stained WSIs (2,051 glomeruli), outperforming the best individual CircleNet model (0.789) and standard NMS/Soft-NMS baselines. They further report a human-in-the-loop annotation study in which annotation time drops from 9.23 to 2.9 minutes per WSI, a 68.59% reduction. The paper's contributions are framed as (1) an accessible QuPath plugin, (2) improved detection via WCF, and (3) an efficient annotation workflow.
Significance. If the reported gains are robust, GloFinder addresses a real usability gap in computational pathology: it turns a research detection model into a clinician-accessible GUI tool with WSI-level processing and online editing, trained on a substantial corpus of ~160,000 manually annotated glomeruli. The open-source plugin, the single-click workflow, and the human-in-the-loop curation concept are all practical contributions that could benefit nephropathology practice. However, the current evidence base is thin: the central detection claim rests on a single table with point estimates from 15 WSIs, no error bars or significance tests, and WCF hyperparameters that appear to be selected without a held-out validation set. The annotation-time reduction also lacks protocol detail. These issues do not invalidate the engineering contribution, but they do mean the quantitative superiority claims are not yet established to the standard expected for a peer-reviewed venue.
major comments (4)
- [Section 3.2 and Table 1] The WCF thresholds T_count=2 and T_score=0.9 are described as 'carefully chosen,' but the manuscript describes no validation split: Section 3.1 states that the 15 PAS-stained WSIs were used 'for testing and evaluation.' If these thresholds were tuned on the same 15 WSIs, the reported mAP of 0.829 is optimistically biased, and the 0.040 mAP improvement over the best single model (0.789) may partly reflect test-set selection rather than a generalizable ensemble gain. Please specify how the thresholds were selected, report the validation data and results if they exist, or provide a sensitivity analysis over the threshold grid showing that the reported operating point is not a cherry-picked optimum.
- [Table 1 and Section 4.1] All detection comparisons are reported as single point estimates without error bars, per-WSI variance, or significance tests. With only 15 test WSIs, the difference between WCF and the best single CircleNet (0.040 mAP) could easily fall within noise. The claim that WCF achieves 'significantly higher' mAP values requires per-WSI mAP distributions, bootstrap confidence intervals, and a paired significance test (e.g., Wilcoxon signed-rank) comparing WCF with each individual model and with NMS/Soft-NMS. Without this, the central detection improvement is not statistically substantiated.
- [Section 4.2] The human-in-the-loop time study lacks the protocol detail needed to support the 68.59% reduction claim. The manuscript does not state how many annotators participated, their experience level, the instructions given, how manual annotation time was measured, what correction operations were counted, or how the 10 WSIs were selected. It also reports only mean times without per-WSI paired comparisons or confidence intervals. Please provide a complete protocol and report the paired per-WSI times with appropriate statistics; otherwise the annotation-efficiency contribution is unverifiable.
- [Code and Demo Video section; Abstract] The abstract states that code and the QuPath plugin are available at https://github.com/hrlblab/GloFinder, but the 'Code and Demo Video' section gives https://github.com/hrlblab/PathVisual. This inconsistency prevents independent verification of the implementation and should be resolved: the manuscript must point to a single, accessible repository containing the plugin, the models, and the evaluation code, or explain the relationship between the two repositories.
minor comments (6)
- [Section 1] The phrase 'a AI-empowered QuPath plugin' should be 'an AI-empowered QuPath plugin.'
- [Table 1 caption] The caption refers to red and blue highlighting for the highest and second-highest values, but the table as rendered in the text may not show color; please add symbolic markers (e.g., superscripts or bold/underline) so the distinctions are visible in grayscale print.
- [Section 2.2] The role of NMS is ambiguous: the text says NMS filters redundant detections before fusion, but it also appears in Table 1 as a standalone ensemble method. Please clarify whether NMS is applied per-model before WCF, after WCF, or both, and define the exact NMS variant and IoU threshold used.
- [Section 3.1] The statement that 'approximately 30,000 glomeruli varied between the training datasets of different models' is vague; please specify the total number of unique glomeruli, the size of the intersection/union across the five training sets, and how the varied subsets were constructed.
- [Section 5] The Discussion lists 'detection time required to process full WSIs' as a limitation, but Section 3.4 reports an average of 21 seconds per WSI; please contextualize whether this is the full WSI processing time or a per-patch time, and clarify what hardware assumption applies.
- [Section 1] The term 'state-of-the-art (SOTA) CircleNet method' is used without comparison to other recent glomerular detection or object detection approaches; please soften the claim or add a brief comparison to justify the SOTA designation.
Circularity Check
No significant circularity: GloFinder's evaluations are empirical and self-contained; self-cited components are described rather than assumed.
full rationale
The paper's central claims are empirical evaluations: Table 1 compares five CircleNet models against NMS, Soft-NMS, and WCF on an independent 15-WSI test set with 2,051 glomeruli. WCF is described operationally (consensus count T=2, score threshold T=0.9) rather than imported as a black box, and the mAP gain is measured, not derived from the WCF definition. CircleNet and WCF are self-cited prior works from the same lab, but those citations provide background; the evaluation is self-contained and the methods are described in Section 2.2 and 3.2. The 'carefully chosen' WCF thresholds are a methodological limitation because no validation split is reported, so the reported gain could partly reflect tuning to the test set; however, the manuscript does not state that thresholds were fitted on these 15 WSIs, and no equation or construction forces the result. The code-link mismatch (abstract: hrlblab/GloFinder; 'Code and Demo Video': hrlblab/PathVisual) is a reproducibility concern, not circularity. Therefore no circular step is exhibited.
Assumptions & free parameters
free parameters (2)
- T count threshold =
2
- T score threshold =
0.9
assumptions (2)
- domain assumption CircleNet's circle representation is sufficient for accurate glomerular detection.
- domain assumption Manual annotations of the 160,000 glomeruli provide accurate ground truth for training and evaluation.
Cite this review
Pith. "Pith review of GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation." pith.science (2026). https://pith.science/paper/OJRMNC5E
@misc{pith2026241118795,
author = {Pith},
title = {Pith review of: GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation},
year = {2026},
howpublished = {\url{https://pith.science/paper/OJRMNC5E}},
note = {Machine review of arXiv:2411.18795}
}
read the original abstract
Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli, the key functional units of the kidney, from whole slide images (WSIs) in kidney pathology. However, existing open-source tools are often distributed as source code or Docker containers, requiring advanced programming skills that hinder accessibility for non-programmers, such as clinicians. Additionally, current models are typically trained on a single dataset and lack flexibility in adjusting confidence levels for predictions. To overcome these challenges, we introduce GloFinder, a QuPath plugin designed for single-click automated glomeruli detection across entire WSIs with online editing through the graphical user interface (GUI). GloFinder employs CircleNet, an anchor-free detection framework utilizing circle representations for precise object localization, with models trained on approximately 160,000 manually annotated glomeruli. To further enhance accuracy, the plugin incorporates Weighted Circle Fusion (WCF), an ensemble method that combines confidence scores from multiple CircleNet models to produce refined predictions, achieving superior performance in glomerular detection. GloFinder enables direct visualization and editing of results in QuPath, facilitating seamless interaction for clinicians and providing a powerful tool for nephropathology research and clinical practice. Code and the QuPath plugin are available at https://github.com/hrlblab/GloFinder
Figures
Forward citations
Cited by 1 Pith paper
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KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level
The KPIs 2024 challenge created a benchmark for glomerular segmentation in PAS-stained mouse kidney slides from four CKD models, and top models achieved Dice scores near 94 percent.
Reference graph
Works this paper leans on
-
[1]
E. H. Nguyen, H. Yang, R. Deng, et al. , ``Circle representation for medical object detection,'' IEEE transactions on medical imaging (3), 746--754 (2021)
work page 2021
-
[2]
P. Bankhead, M. B. Loughrey, J. A. Fern \'a ndez, et al. , ``Qupath: Open source software for digital pathology image analysis,'' Scientific reports (1), 1--7 (2017)
work page 2017
-
[3]
J. Yue, T. Yao, R. Deng, et al. , ``Weighted circle fusion: Ensembling circle representation from different object detection results,'' arXiv preprint arXiv:2406.19540 (2024)
arXiv 2024
-
[4]
M. Zhu, ``Recall, precision and average precision,'' Department of Statistics and Actuarial Science, University of Waterloo, Waterloo (30), 6 (2004)
work page 2004
-
[5]
A. Neubeck and L. Van Gool, ``Efficient non-maximum suppression,'' in 18th International Conference on Pattern Recognition (ICPR'06) , 3 , 850--855 (2006)
work page 2006
- [6]
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[7]
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Reviewed August 12, 2026 · model on record in the stance chip above.
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