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New AI model revolutionizes medical imaging with 90% less computing power

Rice University’s MetaSeg matches U-Net accuracy using 90% fewer parameters, making AI medical image segmentation faster, ...
IEEE

Comparative Analysis of Deep Learning-Based Models for Blood Cell Image Segmentation

Abstract: The accurate segmentation of blood cells is crucial in determining a wide range of hematological disorders. Therefore, in this paper, we investigated the most accurate and efficient blood ...
IEEE

Uncertainty-Driven Edge Prompt Generation Network for Medical Image Segmentation

Abstract: Segment Anything Model (SAM) is a foundational image segmentation model, which shows superior performance for natural image segmentation tasks. Several SAM-based medical image segmentations ...