Enhancing Pathology Image Analysis with Semi-Supervised Learning: A ConvNeXt and U-Net Hybrid Framework for Cancer Diagnosis

Abstract

Digitalpathologyhasexperiencedsignificantadvancementsduetotheincreasingrelianceoncomputationalmodelsformedicaldiagnosisanddiseasedetection. However,amajorobstaclepersists: thescarcityofannotateddatasets required for training deep learning models.Manual annotation by medical experts is time-consuming, expensive, and pronetoerrors, limitingthedevelopmentofrobustdiagnosticsystems.ThisresearchaddressesthischallengebyproposingahybridframeworkthatcombinesSemi-SupervisedLearning(SSL)techniqueswithConvNeXtandU-Netarchitectures to enhance cancer diagnosis using limited labeled data.The study employs SSL strategies such as pseudo-labeling and consistency regularizationtomaximizethe useofunlabeleddatawhileimprovingmodelgeneralization. ConvNeXtserves astheencoderforfeatureextraction, whileU-Netactsasthedecoderforprecisesegmentationtasks.Dataaugmentationtechniquesfurtherenhancetrainingdiversity,reducingoverfittingandimprovinggeneralization.ExperimentalresultsonthePANDAdatasetdemonstratesuperiorperformance,achievingaQuadraticWeightedKappa(QWK)scoreof0.9700,ClinicalAccuracy(ClinAcc)of93%,andAUROCof0.9600.ThesefindingshighlightthepotentialofSSLinovercoming annotation scarcity in digital pathology while paving the way for scalable AI solutions in clinical settings.

Introduction

Background:
Digital pathology, through digitization of histopathological slides into Whole Slide Images (WSIs), enables precise and faster disease diagnosis aided by AI and deep learning (DL). However, the scarcity of annotated datasets—due to costly, time-consuming manual labeling and inter-observer variability—limits the training of robust DL models. Semi-Supervised Learning (SSL), which uses both labeled and unlabeled data, offers a promising solution to this problem by improving model generalization and robustness despite limited annotations.

Problem Statement:
The main challenge is the lack of large, reliable annotated datasets for training DL models in digital pathology. Fully supervised methods require extensive labeled data, which is impractical in real-world medical settings. This research proposes a hybrid framework combining SSL with advanced architectures (ConvNeXt and U-Net) to enable accurate pathology image analysis using minimal labeled data.

Research Objectives:

1. Develop a hybrid ConvNeXt + U-Net model incorporating SSL to analyze pathology images with limited labeled data.

2. Evaluate the model’s performance against traditional supervised approaches using metrics such as Quadratic Weighted Kappa (QWK), AUROC, Mean Absolute Error (MAE), and Clinical Accuracy (ClinAcc).

3. Assess practical applications and scalability in clinical environments.

4. Explore strategies to improve model generalization across diverse datasets using advanced SSL and data augmentation.

Research Questions:

• How can SSL reduce reliance on annotated data in digital pathology?

• What is the impact of integrating ConvNeXt and U-Net with SSL on performance?

• What challenges and opportunities arise when deploying these models clinically?

Significance:
The research addresses the critical bottleneck of annotation scarcity in medical imaging. By combining state-of-the-art DL architectures with SSL, it aims to improve pathology image analysis efficiency and accuracy, potentially accelerating cancer diagnosis and improving patient outcomes. Insights may extend to other medical imaging domains facing similar challenges.

Methodology:

• Quantitative experimental design testing SSL methods on digital pathology datasets.

• Datasets: Camelyon17 (breast cancer WSIs) and PANDA (prostate cancer WSIs with ISUP grades).

• Data preprocessing includes normalization, augmentation, and stratified splits (70% train, 15% validation, 15% test).

• Two architectures developed:

  1. ConvNeXt-XXL model for classification and segmentation.

  2. Hybrid ConvNeXt-XXL + U-Net model incorporating SSL techniques (pseudo-labeling and consistency regularization).

• Models evaluated using QWK, MAE, ClinAcc, and AUROC metrics.

• SSL Implementation: pseudo-labeling generates labels for unlabeled data; consistency regularization enforces prediction stability under input perturbations.

• Training uses AdamW optimizer, one-cycle learning rate scheduler, batch size of 8, for 10 epochs.

Structure:
The paper is organized into Introduction, Related Work, Methodology, Results and Discussion, Comparative Analysis, and Conclusion & Future Work chapters.

Conclusion

Thisresearchpioneerstheuseofsemisupervisedlearning(SSL)withConvNeXtXXLandUNetarchitecturestoovercomeannotationbarriersinmedicalimaging,offeringascalablesolutionfordigitalpathology.ThehybridmodelachievedaQuadraticWeightedKappa(QWK)of0.89andanAUROCof0.94,demonstratingitsabilitytoleveragebothlabeledandunlabeleddataforprostatecancerdiagnosiswithpathologistcomparableaccuracy.Despitechallengesingeneralizationacrossdiversedatasets,highcomputationaldemands,andtheneedforenhancedinterpretability,thefindingslayafoundationforAIdrivenpathology.Futureworkshouldfocusoncrossdatasetvalidation,integratingmultimodaldata,optimizingforreal-timeprocessing,andincorporating explainable AI tools to ensure broader clinical adoption and maximize impact in healthcare.

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Copyright

Copyright © 2025 Badr Elguerch, Youssef Ait Ameur, Veraldo Novaren, Dommane Hamza. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.