Lifelong learning with dynamic convolutions for glioma segmentation from multi-modal MRI

Subhashis Banerjee; Robin Strand

doi:10.1117/12.2654200

3 April 2023 Lifelong learning with dynamic convolutions for glioma segmentation from multi-modal MRI

Subhashis Banerjee, Robin Strand

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124643J (2023) https://doi.org/10.1117/12.2654200
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

This paper presents a novel solution for catastrophic forgetting in lifelong learning (LL) using Dynamic Convolution Neural Network (Dy-CNN). The proposed dynamic convolution layer can adapt convolution filters by learning kernel coefficients or weights based on the input image. The suitability of the proposed Dy-CNN in a lifelong sequential learning-based scenario with multi-modal MR images is experimentally demonstrated for the segmentation of Glioma tumors from multi-modal MR images. Experimental results demonstrated the superiority of the Dy-CNN-based segmenting network in terms of learning through multi-modal MRI images and better convergence of lifelong learning-based training.

Citation Download Citation

Subhashis Banerjee and Robin Strand "Lifelong learning with dynamic convolutions for glioma segmentation from multi-modal MRI", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643J (3 April 2023); https://doi.org/10.1117/12.2654200

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