TinyU-Net

Official implementation of the MICCAI 2024 paper "TinyU-Net: Lighter Yet Better U-Net with Cascaded Multi-receptive Fields".

🔥 This paper has been invited for an ORAL presentation (2.7%) in addition to a POSTER presentation.

BibTex

@InProceedings{Chen_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     = {626--635}
}

Poster

Data

• ISIC2018. The ISIC2018 dataset consists of images with skin disease lesions (2594 training images, 100 validation images, and 1000 test images).
• NCP. Lesion segmentation dataset of the CT slice images from the China Consortium of Chest CT Image Investigation (CC-CCII). A total of 750 CT slices from 150 COVID-19 patients were manually segmented into background, lung field, ground-glass opacity (GGO), and consolidation (CL).

Results