Publications by authors named "Motohide Kawamura"

Article Synopsis
  • This study developed a large multimodality model (LMM) capable of detecting breast and esophageal cancers using chest contrast-enhanced CT scans.
  • A total of 401 patients' CT images were analyzed in training, validation, and testing phases, with the LMM trained on specific cancer-related text data to identify lesions.
  • The fine-tuned LMM demonstrated high sensitivity and diagnostic performance, achieving AUC values of 0.890 and 0.880 for breast and esophageal cancers, respectively, indicating its effectiveness in cancer imaging.
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To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.

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