mlsanityData to 7 Oct 2026

Trans2Unet: Neural fusion for Nuclei Semantic Segmentation

arXiv 2407.17181 ↗First posted 24 Jul 2024eess.IV cs.CVcited by 5

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Abstract (arXiv)

Nuclei segmentation, despite its fundamental role in histopathological image analysis, is still a challenge work. The main challenge of this task is the existence of overlapping areas, which makes separating independent nuclei more complicated. In this paper, we propose a new two-branch architecture by combining the Unet and TransUnet networks for nuclei segmentation task. In the proposed architecture, namely Trans2Unet, the input image is first sent into the Unet branch whose the last convolution layer is removed. This branch makes the network combine features from different spatial regions of the input image and localizes more precisely the regions of interest. The input image is also fed into the second branch. In the second branch, which is called TransUnet branch, the input image will be divided into patches of images. With Vision transformer (ViT) in architecture, TransUnet can serve as a powerful encoder for medical image segmentation tasks and enhance image details by recovering localized spatial information. To boost up Trans2Unet efficiency and performance, we proposed to infuse TransUnet with a computational-efficient variation called "Waterfall" Atrous Spatial Pooling with Skip Connection (WASP-KC) module, which is inspired by the "Waterfall" Atrous Spatial Pooling (WASP) module. Experiment results on the 2018 Data Science Bowl benchmark show the effectiveness and performance of the proposed architecture while compared with previous segmentation models.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 36 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

25 of 36 references.

References · 25

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13 more
  1. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
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  3. 2019ResUNet++: An Advanced Architecture for Medical Image Segmentationcited by 211
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  5. 2022ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyondcited by 117
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  12. 2019Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentationcited by 5
  13. 2021Semantic Segmentation for Urban-Scene Imagescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025VSRM: A Robust Mamba-Based Framework for Video Super-Resolutioncited by 5
  2. 2025ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathologycited by 4
  3. 2026SAT: Selective Aggregation Transformer for Image Super-Resolutioncited by 2
  4. 2025Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusioncited by 1
  5. 2025Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentationcited by 1