# Tree-NET: Enhancing 2D Medical Image Segmentation Through Efficient Low-Level Feature Training

arXiv: https://arxiv.org/abs/2501.02140 · HTML: https://mlsanity.com/paper/2501.02140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 3 Jan 2025.

## Builds on (most cited first, 25 of 25)

- [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)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [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)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (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)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [EfficientFormer: Vision Transformers at MobileNet Speed](https://mlsanity.com/paper/2206.01191/) (2022)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://mlsanity.com/paper/1911.11445/) (2019)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017)
- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS](https://mlsanity.com/paper/2101.07172/) (2021)
- [A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking](https://mlsanity.com/paper/2309.02031/) (2023)
- [DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2310.12570/) (2023)
- [LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation](https://mlsanity.com/paper/1912.06683/) (2019)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- ["Task-relevant autoencoding" enhances machine learning for human neuroscience](https://mlsanity.com/paper/2208.08478/) (2022)

## Abstract (arXiv)

This paper introduces Tree-NET, a novel framework for medical image segmentation that leverages bottleneck supervision to enhance both segmentation accuracy and computational efficiency. While previous studies have applied bottleneck feature supervision to segmentation tasks, it has typically been limited to the training phase, offering no computational benefits during inference. To the best of our knowledge, this is the first framework to employ dual bottleneck supervision for segmentation, leveraging latent space features at both the input and output stages. This approach reduces input and label dimensions with minimal parameter overhead while preserving accuracy. Tree-NET features a three-component architecture: Encoder-Net and Decoder-Net, which compress input and label data via autoencoding, and Bridge-Net, a segmentation model trained on these compressed representations. By operating entirely on dense, low-dimensional features, Tree-NET improves runtime efficiency and can be integrated into existing segmentation models without modifying their internal structures or increasing model size. We evaluate Tree-NET on two key segmentation tasks: skin lesion and polyp segmentation using various backbone models, including U-NET, U-NET++, and Polyp-PVT. Experimental results show that Tree-NET reduces FLOPs by a factor of 4 to 13 and decreases memory usage while maintaining segmentation accuracy comparable to baseline models. For example, with an untrained U-NET++ backbone, Tree-NET improves the Dice score on ISIC 2018 from 0.829 to 0.862 and the IoU from 0.736 to 0.787. On CVC-ClinicDB, it achieves a Dice score of 0.946 and an IoU of 0.901 using a Polyp-PVT backbone, matching or surpassing baseline performance. These findings underscore Tree-NET's potential as a robust and efficient solution for medical image segmentation.