# 1M parameters are enough? A lightweight CNN-based model for medical image segmentation

arXiv: https://arxiv.org/abs/2306.16103 · HTML: https://mlsanity.com/paper/2306.16103/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [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)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1911.07067/) (2019)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers](https://mlsanity.com/paper/2108.06932/) (2021)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition](https://mlsanity.com/paper/2106.12368/) (2021)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)

## Built on by (most cited first, 15 of 15)

- [DreamMapping: High-Fidelity Text-to-3D Generation via Variational Distribution Mapping](https://mlsanity.com/paper/2409.05099/) (2024)
- [RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report](https://mlsanity.com/paper/2506.01947/) (2025)
- [DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights](https://mlsanity.com/paper/2510.14383/) (2025)
- [UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation](https://mlsanity.com/paper/2512.21584/) (2025)
- [MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation](https://mlsanity.com/paper/2412.01405/) (2024)
- [LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement](https://mlsanity.com/paper/2406.14973/) (2024)
- [The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results](https://mlsanity.com/paper/2408.09615/) (2024)
- [Efficient Deep Learning for Medical Imaging: Bridging the Gap Between High-Performance AI and Clinical Deployment](https://mlsanity.com/paper/2602.00910/) (2026)
- [LiteNeXt: A Novel Lightweight ConvMixer-based Model with Self-embedding Representation Parallel for Medical Image Segmentation](https://mlsanity.com/paper/2405.15779/) (2024)
- [Assessment of Using Synthetic Data in Brain Tumor Segmentation](https://mlsanity.com/paper/2508.11922/) (2025)
- [TM-UNet: Token-Memory Enhanced Sequential Modeling for Efficient Medical Image Segmentation](https://mlsanity.com/paper/2511.12270/) (2025)
- [DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy](https://mlsanity.com/paper/2605.16519/) (2026)
- [APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms](https://mlsanity.com/paper/2606.30577/) (2026)
- [Lightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller](https://mlsanity.com/paper/2607.03062/) (2026)
- [Parameter-Efficient 3D Segmentation of Liver and Liver tumors: Depthwise factorization Scales Better Than Dense Convolution with Spatial Dimensionality](https://mlsanity.com/paper/2609.33077/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) and Transformer-based models are being widely applied in medical image segmentation thanks to their ability to extract high-level features and capture important aspects of the image. However, there is often a trade-off between the need for high accuracy and the desire for low computational cost. A model with higher parameters can theoretically achieve better performance but also result in more computational complexity and higher memory usage, and thus is not practical to implement. In this paper, we look for a lightweight U-Net-based model which can remain the same or even achieve better performance, namely U-Lite. We design U-Lite based on the principle of Depthwise Separable Convolution so that the model can both leverage the strength of CNNs and reduce a remarkable number of computing parameters. Specifically, we propose Axial Depthwise Convolutions with kernels 7x7 in both the encoder and decoder to enlarge the model receptive field. To further improve the performance, we use several Axial Dilated Depthwise Convolutions with filters 3x3 for the bottleneck as one of our branches. Overall, U-Lite contains only 878K parameters, 35 times less than the traditional U-Net, and much more times less than other modern Transformer-based models. The proposed model cuts down a large amount of computational complexity while attaining an impressive performance on medical segmentation tasks compared to other state-of-the-art architectures. The code will be available at: https://github.com/duong-db/U-Lite.