首页|Sisli Hamidiye Etfal Education and Research Hospital Reports Findings in Artificial Intelligence [Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis]
Sisli Hamidiye Etfal Education and Research Hospital Reports Findings in Artificial Intelligence [Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis]
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New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Istanbul, Turkey, by NewsRx journalists, research stated, “Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there’s a lack of quantitative evidence regarding their performance.” The news reporters obtained a quote from the research from Sisli Hamidiye Etfal Education and Research Hospital, “To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms. Three radiologists drew ground-truth boxes on a balanced mammogram dataset of women (n = 1496 cancer-positive and negative scans) from three centers. A modified, pre-trained DL model was employed for breast cancer detection, using MLO and CC images. Saliency XAI methods, including Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM, were evaluated. We utilized the Pointing Game to assess these methods, determining if the maximum value of a saliency map aligned with the bounding boxes, representing the ratio of correctly identified lesions among all cancer patients, with a value ranging from 0 to 1. The development sample included 2,244 women (75%), with the remaining 748 women (25%) in the testing set for unbiased XAI evaluation. The model’s recall, precision, accuracy, and F1-Score in identifying cancer in the testing set were 69%, 88%, 80%, and 0.77, respectively. The Pointing Game Scores for Grad-CAM, Grad-CAM++, and Eigen-CAM were 0.41, 0.30, and 0.35 in women with cancer and marginally increased to 0.41, 0.31, and 0.36 when considering only true-positive samples.”
IstanbulTurkeyEurasiaArtificial IntelligenceBreast Cancer ScreeningCancerDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineMachine LearningMammogramMammographyOncologyRisk and PreventionWomen’s Health
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