bibtext: @InProceedings{ Che_TinyUNet_MICCAI2024,
                   author = { Chen, Junren and Chen, Rui and Wang, Wei and Cheng, Junlong and Zhang, Lei and Chen, Liangyin },
                   title = { { TinyU-Net: Lighter yet Better U-Net with Cascaded Multi-Receptive Fields } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: The lightweight models for automatic medical image segmentation have the potential to advance health equity, particularly in limited-resource settings. Nevertheless, their reduced parameters and computational complexity compared to state-of-the-art methods often result in inadequate feature representation, leading to suboptimal segmentation performance. To this end, We propose a Cascade Multi-Receptive Fields (CMRF) module and develop a lighter yet better U-Net based on CMRF, named TinyU-Net, comprising only 0.48M parameters. Specifically, the CMRF module leverages redundant information across multiple channels in the feature map to explore diverse receptive fields by a cost-friendly cascading strategy, improving feature representation while maintaining the lightweightness of the model, thus enhancing performance.  Testing CMRF-based TinyU-Net on cost-effective medical image segmentation datasets demonstrates superior performance with significantly fewer parameters and computational complexity compared to state-of-the-art methods.  For instance, in the lesion segmentation of the ISIC2018 dataset, TinyU-Net is 52x, 3x, and 194x fewer parameters, respectively, while being +3.90%, +3.65%, and +1.05% higher IoU score than baseline U-Net, lightweight UNeXt, and high-performance TransUNet, respectively.  Notably, the CMRF module exhibits adaptability, easily integrating into other networks. Experimental results suggest that TinyU-Net, with its outstanding performance, holds the potential to be implemented in limited-resource settings, thereby contributing to health equity. The code is available at https://github.com/ChenJunren-Lab/TinyU-Net.
title: TinyU-Net: Lighter yet Better U-Net with Cascaded Multi-Receptive Fields
authors: Chen, Junren Chen, Rui Wang, Wei Cheng, Junlong Zhang, Lei Chen, Liangyin