mlsanityData to 7 Oct 2026

PVTAdpNet: Polyp Segmentation using Pyramid vision transformer with a novel Adapter block

arXiv 2509.23751 ↗First posted 28 Sep 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Colorectal cancer ranks among the most common and deadly cancers, emphasizing the need for effective early detection and treatment. To address the limitations of traditional colonoscopy, including high miss rates due to polyp variability, we introduce the Pyramid Vision Transformer Adapter Residual Network (PVTAdpNet). This model integrates a U-Net-style encoder-decoder structure with a Pyramid Vision Transformer backbone, novel residual blocks, and adapter-based skip connections. The design enhances feature extraction, dense prediction, and gradient flow, supported by squeeze-and-excitation attention for improved channel-wise feature refinement. PVTAdpNet achieves real-time, accurate polyp segmentation, demonstrating superior performance on benchmark datasets with high mDice and mIoU scores, making it highly suitable for clinical applications. PVTAdpNet obtains a high Dice coefficient of 0.8851 and a mean Intersection over Union (mIoU) of 0.8167 on out-of-distribution polyp datasets. Evaluation of the PolypGen dataset demonstrates PVTAdpNet's capability for real-time, accurate performance within familiar distributions. The source code of our network is available at https://github.com/ayousefinejad/PVTAdpNet.git

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 28 Sep 2025.

Builds on

33 of 50 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2017Squeeze-and-Excitation Networkscited by 4,763
  6. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  7. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  8. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  9. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  10. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
  11. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 588
  12. 2017Learning multiple visual domains with residual adapterscited by 553
21 more
  1. 2020PraNet: Parallel Reverse Attention Network for Polyp Segmentationcited by 460
  2. 2020ResNeSt: Split-Attention Networkscited by 445
  3. 2017Road Extraction by Deep Residual U-Netcited by 333
  4. 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 317
  5. 2021Understanding Robustness of Transformers for Image Classificationcited by 250
  6. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  7. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  8. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  9. 2017Incremental Learning Through Deep Adaptationcited by 108
  10. 2019Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practicecited by 71
  11. 2021HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPScited by 71
  12. 2021A multi-centre polyp detection and segmentation dataset for generalisability assessmentcited by 63
  13. 2021UACANet: Uncertainty Augmented Context Attention for Polyp Segmentationcited by 61
  14. 2018Polyp Segmentation in Colonoscopy Images Using Fully Convolutional Networkcited by 33
  15. 2013Automated polyp detection in colon capsule endoscopycited by 30
  16. 2022ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentationcited by 28
  17. 2023TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testingcited by 26
  18. 2021Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networkscited by 10
  19. 2019A Survey on Contemporary Computer-Aided Tumor, Polyp, and Ulcer Detection Methods in Wireless Capsule Endoscopy Imagingcited by 7
  20. 2023Meta-Polyp: a baseline for efficient Polyp segmentationcited by 6
  21. 2017Superpixel Based Segmentation and Classification of Polyps in Wireless Capsule Endoscopycited by 5

Built on by

Not cited yet.