Scholarlink

<p>Pulmonary embolism (PE) is diagnosed early and accurately to ensure minimal danger at an advanced stage. This approach extends the advanced techniques for preprocessing, including normalization, slice filtering and resizing. It combines an architecture with skip connections and upsampling toward capturing that extensive detailed contextual information. The loss function used in the model is a combination of SSIM and Dice loss to balance consistency with regard to structural detail and optimization of pixel overlap. It is estimated on a PE challenge dataset (CT scans), where the mean Dice coefficient reached 0.9407, Jaccard similarity 0.9286, sensitivity 0.9324. This methodology outperforms the state of art models. All this shows that the model has a good potential for being applied in clinical practice to automate PE detection.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A novel hybrid loss-based Encoder–Decoder model for accurate Pulmonary Embolism segmentation

  • 期刊论文

  • 发表日期：

    2025年1月11日

查看全文
  • Renu Vadhera,
  • Meghna Sharma

摘要

Pulmonary embolism (PE) is diagnosed early and accurately to ensure minimal danger at an advanced stage. This approach extends the advanced techniques for preprocessing, including normalization, slice filtering and resizing. It combines an architecture with skip connections and upsampling toward capturing that extensive detailed contextual information. The loss function used in the model is a combination of SSIM and Dice loss to balance consistency with regard to structural detail and optimization of pixel overlap. It is estimated on a PE challenge dataset (CT scans), where the mean Dice coefficient reached 0.9407, Jaccard similarity 0.9286, sensitivity 0.9324. This methodology outperforms the state of art models. All this shows that the model has a good potential for being applied in clinical practice to automate PE detection.