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eess.IV2024
MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training
Chengyin Li, Hui Zhu, Rafi Ibn Sultan +5
In the diverse field of medical imaging, automatic segmentation has numerous applications and must handle a wide variety of input domains, such as different types of Computed Tomog…
eess.IV2023★ 2 cited
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation
Mohammad Peivandi, Jason Zhang, Michael Lu +2
Brain tumor segmentation presents a formidable challenge in the field of Medical Image Segmentation. While deep-learning models have been useful, human expert segmentation remains…