June 6, 2024, 4:50 a.m. | Jonghun Kim, Hyunjin Park

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.02936v1 Announce Type: cross
Abstract: Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment customization. Deep learning has shown promise in medical imaging diagnosis, particularly when utilizing multiple imaging modalities to enhance accuracy. This study presents a model that predicts pCR in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps. Radiomics features are established hand-crafted …

abstract arxiv attention cancer cancer treatment cs.cv customization deep learning diagnosis eess.iv imaging medical medical imaging mri multimodal multiple network patient radiomics self-attention treatment type women

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