# TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model

arXiv: https://arxiv.org/abs/2207.13415 · HTML: https://mlsanity.com/paper/2207.13415/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 57 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 30 of 44)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)

## Built on by (most cited first, 30 of 34)

- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.09157/) (2024)
- [H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.13642/) (2024)
- [DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2212.13504/) (2022)
- [Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation](https://mlsanity.com/paper/2210.16898/) (2022)
- [DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2310.12570/) (2023)
- [SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation](https://mlsanity.com/paper/2408.00496/) (2024)
- [DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation](https://mlsanity.com/paper/2308.02959/) (2023)
- [Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach](https://mlsanity.com/paper/2301.10847/) (2023)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture](https://mlsanity.com/paper/2304.11450/) (2023)
- [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://mlsanity.com/paper/2408.00191/) (2024)
- [Merging Context Clustering with Visual State Space Models for Medical Image Segmentation](https://mlsanity.com/paper/2501.01618/) (2025)
- [Performance Evaluation of Swin Vision Transformer Model using Gradient Accumulation Optimization Technique](https://mlsanity.com/paper/2308.00197/) (2023)
- [Deep models for stroke segmentation: do complex architectures always perform better?](https://mlsanity.com/paper/2403.17177/) (2024)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification](https://mlsanity.com/paper/2311.15625/) (2023)
- [A Mutual Inclusion Mechanism for Precise Boundary Segmentation in Medical Images](https://mlsanity.com/paper/2404.08201/) (2024)
- [Q2A: Querying Implicit Fully Continuous Feature Pyramid to Align Features for Medical Image Segmentation](https://mlsanity.com/paper/2404.09472/) (2024)
- [SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation](https://mlsanity.com/paper/2407.10157/) (2024)
- [M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast](https://mlsanity.com/paper/2507.20582/) (2025)
- [Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users](https://mlsanity.com/paper/2511.01990/) (2025)
- [Prithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mapping](https://mlsanity.com/paper/2601.02315/) (2026)
- [RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation](https://mlsanity.com/paper/2409.05280/) (2024)
- [MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation](https://mlsanity.com/paper/2410.22223/) (2024)
- [J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation](https://mlsanity.com/paper/2411.16568/) (2024)
- [Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation](https://mlsanity.com/paper/2504.03108/) (2025)

## Abstract (arXiv)

In the past few years, convolutional neural networks (CNNs), particularly U-Net, have been the prevailing technique in the medical image processing era. Specifically, the seminal U-Net, as well as its alternatives, have successfully managed to address a wide variety of medical image segmentation tasks. However, these architectures are intrinsically imperfect as they fail to exhibit long-range interactions and spatial dependencies leading to a severe performance drop in the segmentation of medical images with variable shapes and structures. Transformers, preliminary proposed for sequence-to-sequence prediction, have arisen as surrogate architectures to precisely model global information assisted by the self-attention mechanism. Despite being feasibly designed, utilizing a pure Transformer for image segmentation purposes can result in limited localization capacity stemming from inadequate low-level features. Thus, a line of research strives to design robust variants of Transformer-based U-Net. In this paper, we propose Trans-Norm, a novel deep segmentation framework which concomitantly consolidates a Transformer module into both encoder and skip-connections of the standard U-Net. We argue that the expedient design of skip-connections can be crucial for accurate segmentation as it can assist in feature fusion between the expanding and contracting paths. In this respect, we derive a Spatial Normalization mechanism from the Transformer module to adaptively recalibrate the skip connection path. Extensive experiments across three typical tasks for medical image segmentation demonstrate the effectiveness of TransNorm. The codes and trained models are publicly available at https://github.com/rezazad68/transnorm.