---
title: "EnBCDet: Explainable Contrastive Self-Supervised Model for Malignant Cell Detection in Urine Cytol"
id: "pubmed-42765133"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42765133"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42765133/"
doi: "10.1088/2057-1976/aea429"
published_at: "2026-09-21T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# EnBCDet: Explainable Contrastive Self-Supervised Model for Malignant Cell Detection in Urine Cytol
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42765133
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42765133/)
- **DOI:** [10.1088/2057-1976/aea429](https://doi.org/10.1088%2F2057-1976%2Faea429)
- **Published At:** 2026-09-21T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Bladder cancer is a common urological malignancy where early detection improves outcomes; **urine cytology** is a noninvasive screening tool but automated analysis faces barriers. - Key challenges for automated urine-cytology systems are the **scarcity of expert-annotated data** and the limited interpretability of deep learning models. - The authors propose EnBCDet, an **explainable self-supervised** framework that uses **contrastive self-supervised learning** to learn discriminative cytological representations from limited annotations. - EnBCDet integrates a specialized backbone within a **single-stage object detection** architecture to enable efficient malignant cell localization in cytology images. - A novel **entropy-based explanation** framework was introduced to quantify information content of activation maps; it is described as **gradient-free, class-agnostic, and task-independent**, allowing interpretation of encoder-level features. - Experimental evaluation used cytological samples from **150 individuals**. - Performance reported: **mean precision 0.991** and **mean recall 0.926**, while retaining computational efficiency. - Clinical validation demonstrated **over 90% agreement** with expert annotations, indicating practical utility. - Authors state this is among the first systematic interpretations of encoder-level representations in self-supervised cytological analysis. - The study states analysis code is publicly available on GitHub, but the source did not report the repository URL or additional implementation details.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Department of Computer Science and Engineering, National Institute of Technology Calicut, NIT Campus, Calicut 673601, Kerala, India. * 2 Department of Electrical and Electronics Engineering, Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi 229304, Uttar Pradesh, India. * 3 Department of Cytology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012 Chandigarh, India. * PMID: **42765133** * DOI: [ 10.1088/2057-1976/aea429 ](https://doi.org/10.1088/2057-1976/aea429) Item in Clipboard # Task-agnostic explainable contrastive learning model for malignant cell detection in urine cytology Arathy Menon N P et al. Biomed Phys Eng Express. 2026. Show details Display options Display options Format Abstract PubMed PMID Biomed Phys Eng Express Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Biomed+Phys+Eng+Express%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Biomed+Phys+Eng+Express%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42765133/) . 2026 Sep 21;12(5). doi: 10.1088/2057-1976/aea429. ### Authors [Arathy Menon N P](https://pubmed.ncbi.nlm.nih.gov/?term=N+P+AM&cauthor_id=42765133)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42765133/#short-view-affiliation-1 "Department of Computer Science and Engineering, National Institute of Technology Calicut, NIT Campus, Calicut 673601, Kerala, India."), [Ram S Iyer](https://pubmed.ncbi.nlm.nih.gov/?term=Iyer+RS&cauthor_id=42765133)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42765133/#short-view-affiliation-2 "Department of Electrical and Electronics Engineering, Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi 229304, Uttar Pradesh, India."), [Pournami P N](https://pubmed.ncbi.nlm.nih.gov/?term=N+PP&cauthor_id=42765133)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42765133/#short-view-affiliation-1 "Department of Computer Science and Engineering, National Institute of Technology Calicut, NIT Campus, Calicut 673601, Kerala, India."), [Jayaraj P B](https://pubmed.ncbi.nlm.nih.gov/?term=B+JP&cauthor_id=42765133)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42765133/#short-view-affiliation-1 "Department of Computer Science and Engineering, National Institute of Technology Calicut, NIT Campus, Calicut 673601, Kerala, India."), [Pranab Dey](https://pubmed.ncbi.nlm.nih.gov/?term=Dey+P&cauthor_id=42765133)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42765133/#short-view-affiliation-3 "Department of Cytology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012 Chandigarh, India.") ### Affiliations * 1 Department of Computer Science and Engineering, National Institute of Technology Calicut, NIT Campus, Calicut 673601, Kerala, India. * 2 Department of Electrical and Electronics Engineering, Rajiv Gandhi Institute of Petroleum Technology, Jais, Amethi 229304, Uttar Pradesh, India. * 3 Department of Cytology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh, 160012 Chandigarh, India. * PMID: **42765133** * DOI: [ 10.1088/2057-1976/aea429 ](https://doi.org/10.1088/2057-1976/aea429) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract Bladder cancer remains one of the most prevalent urological malignancies, where early detection is critical for improving patient outcomes. Urine cytology provides a non-invasive and cost-effective screening modality; however, the development of automated diagnostic systems is challenged by the scarcity of expert-annotated data and the limited interpretability of deep learning models. To address these challenges, we propose EnBCDet, an explainable self-supervised framework for malignant cell detection in urine cytology images. The proposed approach leverages contrastive self-supervised learning to learn discriminative cytological representations from limited annotations and incorporates a specialized backbone within a single-stage object detection architecture for efficient malignant cell localization. In addition, we introduce a novel entropy-based explanation framework that quantitatively evaluates the information content of activation maps, enabling gradient-free, class-agnostic, and task-independent interpretation of learned feature representations. Experimental evaluation on cytological samples collected from 150 individuals demonstrates that EnBCDet outperforms existing approaches, achieving a mean precision of 0.991 and a mean recall of 0.926 while maintaining computational efficiency. Clinical validation further shows over 90% agreement with expert annotations, highlighting the practical utility of the proposed system. To the best of our knowledge, this is among the first studies to systematically interpret encoder-level representations in self-supervised cytological analysis. The proposed entropy-based framework provides robust insights into backbone feature learning, advancing both diagnostic performance and explainability for computer-aided urine cytology. The analysis code supporting this study is publicly available on GitHub at. **Keywords:** bladder cancer; deep learning; explainable AI; object detection; self supervised contrastive learning; urine Cytology. © 2026 IOP Publishing Ltd. 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