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Related Experiment Video

Updated: Jan 10, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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ClinSegNet: Towards Reliable and Enhanced Histopathology Screening.

Boyang Yu1, Hannah Markham2,3, Karwan Moutasim2

  • 1School of Electronics and Computer Science (ECS), University of Southampton, Southampton SO17 1BJ, UK.

Bioengineering (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

ClinSegNet enhances histopathology screening by prioritizing lesion detection recall, crucial for reducing missed diagnoses in clinical settings. This framework improves accuracy for small or indistinct lesions.

Keywords:
clinical screeningdeep learninghigh recallhistopathology segmentationhuman-centric framework

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Area of Science:

  • Digital pathology
  • Medical image analysis
  • Computational oncology

Background:

  • Histopathological image segmentation methods struggle with small lesions and unclear boundaries, leading to missed detections.
  • Missed diagnoses in clinical settings carry more severe consequences than false positives.

Purpose of the Study:

  • To develop ClinSegNet, a recall-oriented, human-centered framework for reliable histopathology screening.
  • To improve sensitivity for small lesions and indistinct boundaries in histopathological images.

Main Methods:

  • ClinSegNet utilizes a composite optimization strategy (HistoLoss) balancing stability, boundary refinement, and recall.
  • An uncertainty-driven refinement mechanism targets high-uncertainty cases for efficient fine-tuning.
  • A clinical data processing pipeline derived pixel-level annotations from IHC-to-H&E mapping for training with limited data.

Main Results:

  • ClinSegNet achieved high recall scores (0.8803-0.8917), further improved with human-in-the-loop (HITL) refinement (0.8983-0.9053).
  • The framework maintained competitive Dice and IoU scores, demonstrating effective lesion capture.
  • Ablation studies confirmed the framework's advantage in detecting small or complex lesions.

Conclusions:

  • ClinSegNet offers a clinically oriented, recall-prioritized framework to enhance lesion coverage and reduce missed diagnoses.
  • The study provides a methodological basis for future human-in-the-loop systems and a pipeline for leveraging limited clinical data.