Keywords: Machine Learning/Artificial Intelligence, PET/MR, Automated Segmentation, Deep Learning, TransformersAutomated segmentation of tumors and designing a computer-assisted diagnosis system requires co-learning of imaging features from the complementary PET/MRI. We aim to develop an automated method for the segmentation of cancer-affected lymph nodes on Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI) using a modified Swin Transformer model (ST) integrated with a novel Multi-Scale Feature Fusion & Reorganization Module (MSFFRM). Our results with Hodgkin lymphoma (HL) and Non-Hodgkin lymphoma subtype Diffuse Large B-Cell Lymphoma (DLBCL) datasets show that our proposed model ST-MSFFRM achieved better performance in lesion segmentation in comparison to other state-of-the-art segmentation methods.
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