# Med-TTT: Vision Test-Time Training model for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2410.02523 · HTML: https://mlsanity.com/paper/2410.02523/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [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)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Learning to (Learn at Test Time): RNNs with Expressive Hidden States](https://mlsanity.com/paper/2407.04620/) (2024)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation](https://mlsanity.com/paper/2211.01784/) (2022)
- [HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation](https://mlsanity.com/paper/2405.05007/) (2024)

## Built on by (most cited first, 3 of 3)

- [Enhancing Feature Fusion of U-like Networks with Dynamic Skip Connections](https://mlsanity.com/paper/2509.14610/) (2025)
- [TTTFusion: A Test-Time Training-Based Strategy for Multimodal Medical Image Fusion in Surgical Robots](https://mlsanity.com/paper/2504.20362/) (2025)
- [FAN-Unet: Enhancing Unet with vision Fourier Analysis Block for Biomedical Image Segmentation](https://mlsanity.com/paper/2411.18975/) (2024)

## Abstract (arXiv)

Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning. Although models based on convolutional neural networks (CNNs) and Transformers have achieved remarkable success in medical image segmentation tasks, they still face challenges such as high computational complexity and the loss of local features when capturing long-range dependencies. To address these limitations, we propose Med-TTT, a visual backbone network integrated with Test-Time Training (TTT) layers, which incorporates dynamic adjustment capabilities. Med-TTT introduces the Vision-TTT layer, which enables effective modeling of long-range dependencies with linear computational complexity and adaptive parameter adjustment during inference. Furthermore, we designed a multi-resolution fusion mechanism to combine image features at different scales, facilitating the identification of subtle lesion characteristics in complex backgrounds. At the same time, we adopt a frequency domain feature enhancement strategy based on high pass filtering, which can better capture texture and fine-grained details in images. Experimental results demonstrate that Med-TTT significantly outperforms existing methods on multiple medical image datasets, exhibiting strong segmentation capabilities, particularly in complex image backgrounds. The model achieves leading performance in terms of accuracy, sensitivity, and Dice coefficient, providing an efficient and robust solution for the field of medical image segmentation.The code is available at https://github.com/Jiashu-Xu/Med-TTT .