arXiv:2307.11274
공식 코드 · Papers with Code
Screening Mammography Breast Cancer Detection
원문 초록
Breast cancer is a leading cause of cancer-related deaths, but current programs are expensive and prone to false positives, leading to unnecessary follow-up and patient anxiety. This paper proposes a solution to automated breast cancer detection, to improve the efficiency and accuracy of screening programs. Different methodologies were tested against the RSNA dataset of radiographic breast images of roughly 20,000 female patients and yielded an average validation case pF1 score of 0.56 across methods.
함께 살펴볼 논문
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Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation
Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (MWR) Breast Cancer Detection
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
BCDNet: A Fast Residual Neural Network For Invasive Ductal Carcinoma Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.