mlsanityData to 7 Oct 2026

SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images

arXiv 2312.01128 ↗First posted 2 Dec 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Accurate identification and precise delineation of regions of significance, such as tumors or lesions, is a pivotal goal in medical imaging analysis. This paper proposes SPEEDNet, a novel architecture for precisely segmenting lesions within colonoscopy images. SPEEDNet uses a novel block named Dilated-Involutional Pyramidal Convolution Fusion (DIPC). A DIPC block combines the dilated involution layers pairwise into a pyramidal structure to convert the feature maps into a compact space. This lowers the total number of parameters while improving the learning of representations across an optimal receptive field, thereby reducing the blurring effect. On the EBHISeg dataset, SPEEDNet outperforms three previous networks: UNet, FeedNet, and AttesResDUNet. Specifically, SPEEDNet attains an average dice score of 0.952 and a recall of 0.971. Qualitative results and ablation studies provide additional insights into the effectiveness of SPEEDNet. The model size of SPEEDNet is 9.81 MB, significantly smaller than that of UNet (22.84 MB), FeedNet(185.58 MB), and AttesResDUNet (140.09 MB).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 2 Dec 2023.

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11 of 19 references.

References · 11

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  3. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
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  5. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
  6. 2018A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentationcited by 171
  7. 2018MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Imagescited by 87
  8. 2023Using DUCK-Net for Polyp Image Segmentationcited by 30
  9. 2019Attention Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Imagescited by 17
  10. 2022EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Taskscited by 15
  11. 2023AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Netcited by 4

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