Publications by authors named "Xinmi Huo"
Article Synopsis
- AI solutions for Gleason grading show potential for pathologists, but face challenges like inconsistent image quality and limited adaptability to different data sources.
- The proposed digital pathology workflow includes AI-driven components for image quality control, cloud annotation, and ongoing model improvements, achieving promising results across various scanner types.
- The model notably improves Gleason scoring speed by 43% and enhances accuracy, making it a significant step towards integrating AI in clinical practices for better diagnostic consistency.
Motivation: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error.
View Article and Find Full Text PDFMotivation: Differentiating 12 stages of the mouse seminiferous epithelial cycle is vital towards understanding the dynamic spermatogenesis process. However, it is challenging since two adjacent spermatogenic stages are morphologically similar. Distinguishing Stages I-III from Stages IV-V is important for histologists to understand sperm development in wildtype mice and spermatogenic defects in infertile mice.
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