mlsanityData to 14 Oct 2026

Improved Segmentation of Polyps and Visual Explainability Analysis

arXiv 2509.18159 ↗First posted 17 Sep 2025cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, with gastrointestinal (GI) polyps serving as critical precursors according to the World Health Organization (WHO). Early and accurate segmentation of polyps during colonoscopy is essential for reducing CRC progression, yet manual delineation is labor-intensive and prone to observer variability. Deep learning methods have demonstrated strong potential for automated polyp analysis, but their limited interpretability remains a barrier to clinical adoption. In this study, we present PolypSeg-GradCAM, an explainable deep learning framework that integrates a U-Net architecture with a pre-trained ResNet-34 backbone and Gradient-weighted Class Activation Mapping (Grad-CAM) for transparent polyp segmentation. To ensure rigorous benchmarking, the model was trained and evaluated using 5-Fold Cross-Validation on the Kvasir-SEG dataset of 1,000 annotated endoscopic images. Experimental results show a mean Dice coefficient of 0.8902 +/- 0.0125, a mean Intersection-over-Union (IoU) of 0.8023, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9722. Advanced quantitative analysis using an optimal threshold yielded a Sensitivity of 0.9058 and Precision of 0.9083. Additionally, Grad-CAM visualizations confirmed that predictions were guided by clinically relevant regions, offering insight into the model's decision-making process. This study demonstrates that integrating segmentation accuracy with interpretability can support the development of trustworthy AI-assisted colonoscopy tools.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 17 Sep 2025.

Builds on

17 of 41 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  5. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  6. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  7. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  8. 2021TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentationcited by 60
  9. 2020Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopycited by 54
  10. 2023CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objectscited by 43
  11. 2023From CNN to Transformer: A Review of Medical Image Segmentation Modelscited by 22
  12. 2024ASPS: Augmented Segment Anything Model for Polyp Segmentationcited by 12
5 more
  1. 2021Weighted Intersection over Union (wIoU) for Evaluating Image Segmentationcited by 11
  2. 2023Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challengescited by 3
  3. 2025LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Imagescited by 3
  4. 2025Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detectioncited by 2
  5. 2025TransUNet-GradCAM: A Hybrid Transformer-U-Net with Self-Attention and Explainable Visualizations for Foot Ulcer Segmentationcited by 2

Built on by

Not cited yet.