Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images
Pith reviewed 2026-05-18 11:33 UTC · model grok-4.3
The pith
A hybrid model fuses mathematical clustering with U-Net insights to segment choroidal nevi at 89.7% Dice on high-resolution fundus images.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that a hybrid model merging mathematical/clustering segmentation with U-Net insights simultaneously raises accuracy, lowers the volume of required training data, and produces strong results on high-resolution fundus images, reaching a Dice coefficient of 89.7 percent and an IoU of 80.01 percent while outperforming Attention U-Net.
What carries the argument
The hybrid segmentation model that integrates mathematical and clustering methods with U-Net insights to leverage the precision of classical techniques alongside learned features from deep learning.
If this is right
- Enables more reliable automated outlining of choroidal nevi to support early melanoma risk assessment.
- Lowers the annotated data volume needed to train effective medical segmentation models.
- Delivers better results on external datasets, aiding deployment across varied clinical sites.
- Accelerates lesion annotation to shorten diagnosis and monitoring workflows for ophthalmologists.
Where Pith is reading between the lines
- Hybrid classical-plus-deep-learning designs may help other data-scarce medical imaging tasks where pure neural nets overfit.
- The same fusion strategy could be tested on different resolutions or lesion types to map its operating range.
- Embedding the model in real-time screening pipelines might reduce diagnostic delays for pigmented eye lesions.
Load-bearing premise
The premise that mathematical and clustering segmentation can be combined with U-Net insights without introducing new failure modes or losing the individual benefits of either component on high-resolution images.
What would settle it
A head-to-head test on a large external fundus dataset in which the hybrid model shows lower Dice or IoU scores than Attention U-Net, or degrades on specific image qualities, would directly challenge the performance and generalizability claims.
read the original abstract
Choroidal nevi are common benign pigmented lesions in the eye, with a small risk of transforming into melanoma. Early detection is critical to improving survival rates, but misdiagnosis or delayed diagnosis can lead to poor outcomes. Despite advancements in AI-based image analysis, diagnosing choroidal nevi in colour fundus images remains challenging, particularly for clinicians without specialized expertise. Existing datasets often suffer from low resolution and inconsistent labelling, limiting the effectiveness of segmentation models. This paper addresses the challenge of achieving precise segmentation of fundus lesions, a critical step toward developing robust diagnostic tools. While deep learning models like U-Net have demonstrated effectiveness, their accuracy heavily depends on the quality and quantity of annotated data. Previous mathematical/clustering segmentation methods, though accurate, required extensive human input, making them impractical for medical applications. This paper proposes a novel approach that combines mathematical/clustering segmentation models with insights from U-Net, leveraging the strengths of both methods. This hybrid model improves accuracy, reduces the need for large-scale training data, and achieves significant performance gains on high-resolution fundus images. The proposed model achieves a Dice coefficient of 89.7% and an IoU of 80.01% on 1024*1024 fundus images, outperforming the Attention U-Net model, which achieved 51.3% and 34.2%, respectively. It also demonstrated better generalizability on external datasets. This work forms a part of a broader effort to develop a decision support system for choroidal nevus diagnosis, with potential applications in automated lesion annotation to enhance the speed and accuracy of diagnosis and monitoring.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper proposes a novel hybrid approach for segmenting lesions in fundus images by integrating mathematical and clustering segmentation models with insights from U-Net architectures. The hybrid model is claimed to enhance accuracy, minimize the requirement for extensive training data, and deliver strong results on high-resolution 1024x1024 images, with a Dice coefficient of 89.7% and IoU of 80.01%, surpassing Attention U-Net's 51.3% Dice and 34.2% IoU, while showing improved generalizability on external datasets. The work contributes to a decision support system for choroidal nevus diagnosis.
Significance. If the empirical results hold under scrutiny, this hybrid strategy could offer a practical solution to data scarcity issues in medical imaging by blending the precision of traditional methods with the automation of deep learning. It has potential significance for improving diagnostic accuracy in ophthalmology, particularly for early detection of conditions like choroidal nevi that carry risks of malignancy.
major comments (2)
- The abstract reports a Dice coefficient of 89.7% and IoU of 80.01% for the proposed model versus 51.3% and 34.2% for Attention U-Net on 1024*1024 fundus images, but provides no information on the hybrid architecture, the mathematical/clustering components, dataset characteristics, training procedure, or validation protocol. This omission is load-bearing as it precludes verification of the central performance claims and the assertion of reduced need for large-scale training data.
- The claim of better generalizability on external datasets is stated without specifying the external datasets used, the metrics on those datasets, or any cross-validation details, undermining the ability to assess this aspect of the contribution.
Simulated Author's Rebuttal
We thank the referee for their insightful comments on our manuscript. We address each of the major comments in detail below and indicate the revisions we will make to strengthen the paper.
read point-by-point responses
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Referee: The abstract reports a Dice coefficient of 89.7% and IoU of 80.01% for the proposed model versus 51.3% and 34.2% for Attention U-Net on 1024*1024 fundus images, but provides no information on the hybrid architecture, the mathematical/clustering components, dataset characteristics, training procedure, or validation protocol. This omission is load-bearing as it precludes verification of the central performance claims and the assertion of reduced need for large-scale training data.
Authors: We recognize that the abstract's length constraints limited the inclusion of detailed information on the hybrid architecture, components, dataset, training, and validation. The full manuscript provides comprehensive descriptions in the relevant sections: the hybrid model combining mathematical/clustering with U-Net insights is detailed in the Methods, dataset characteristics and high-resolution images in Data, and training/validation protocols in Experiments. To address the referee's concern and facilitate verification, we will revise the abstract to incorporate a concise summary of the hybrid approach, key dataset features, and validation strategy. This will better support the performance claims and the reduced data requirement assertion. revision: yes
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Referee: The claim of better generalizability on external datasets is stated without specifying the external datasets used, the metrics on those datasets, or any cross-validation details, undermining the ability to assess this aspect of the contribution.
Authors: We agree that more details are necessary to substantiate the generalizability claim. While the manuscript reports improved performance on external datasets, we will revise the text to explicitly name the external datasets, provide the specific metrics obtained, and outline the cross-validation procedures used. These additions will be made in the Results and Discussion sections, with a brief mention in the abstract if space permits. This will allow readers to fully evaluate the generalizability aspect of our contribution. revision: yes
Circularity Check
No significant circularity detected
full rationale
The provided abstract contains no equations, derivations, parameter fittings, or mathematical claims that could reduce to self-definition or fitted inputs. Performance metrics (Dice 89.7%, IoU 80.01%) are presented strictly as empirical outcomes of the hybrid model on 1024x1024 images, with no indication that these quantities are constructed from the inputs by definition. No self-citations, uniqueness theorems, or ansatzes are invoked in a load-bearing way. The text is therefore self-contained against external benchmarks with no identifiable circular steps.
discussion (0)
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