ArticleJournal of advanced research2025
Generalized deep learning for histopathology image classification using supervised contrastive learning.
Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.Neuroinformatics · 2026Article
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
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3 authors.
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Abstract
introductionCancer is a leading cause of death worldwide, necessitating effective diagnostic tools for early detection and treatment. Histopathological image analysis is crucial for cancer diagnosis but is often hindered by human error and variability. This study introduces HistopathAI, a hybrid network designed for histopathology image classification, aimed at enhancing diagnostic precision and efficiency in clinical pathology.
objectivesThe primary goal of this study is to demonstrate that HistopathAI, leveraging supervised contrastive learning (SCL) and hybrid deep feature fusion (HDFF), can significantly improve the accuracy of histopathological image classification, including scenarios involving imbalanced datasets.
methodsHistopathAI integrates features from EfficientNetB3 and ResNet50, using HDFF to provide a rich representation of histopathology images. The framework employs a sequential methodology, transitioning from feature learning to classifier learning, mirroring the essence of contrastive learning with the aim of producing superior feature representations. The model combines SCL for feature representation with cross-entropy (CE) loss for classification. We evaluated HistopathAI across seven publicly available datasets and one private dataset, covering various histopathology domains.
resultsHistopathAI achieved state-of-the-art classification accuracy across all datasets, demonstrating superior performance in both binary and multiclass classification tasks. Statistical testing confirmed that HistopathAI's performance is significantly better than baseline models, ensuring robust and reliable improvements.
conclusionHistopathAI offers a robust tool for histopathology image classification, enhancing diagnostic accuracy and supporting the transition to digital pathology. This framework has the potential to improve cancer diagnosis and patient outcomes, paving the way for broader clinical application. The code is available on https://github.com/Mamunur-20/HistopathAI.
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