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        <datestamp>2026-10-30T06:48:35Z</datestamp>
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        <dc:title>Two-Stage Transfer Learning for Slice-Level Pancreatic CT Classification: Performance Benchmarking, Confidence-Interval Reporting, Saliency Audit and a Data-Leakage Risk Analysis</dc:title>
        <dc:creator>vemulawada Navyasri</dc:creator>
        <dc:subject>pancreatic cancer; computed tomography; deep learning; transfer learning; EfficientNet;  Grad-CAM; data leakage; medical image classification</dc:subject>
        <dc:description>Transfer learning with partial fine-tuning separates the two classes almost perfectly on a
slice-level split, and fine-tuning EfficientNet-B0 removes the baseline&apos;s specificity failure. However, the
reported figures should be read as an upper bound on clinical performance until they are reproduced with
patient-level partitioning and external data. We provide a reproducible leakage-aware protocol and a
reporting checklist for slice-based pancreatic CT classifiers.</dc:description>
        <dc:publisher>Life of Research</dc:publisher>
        <dc:date>2026-10-30</dc:date>
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        <dc:identifier>https://lifeofresearch.com/articles/159</dc:identifier>
        <dc:language>eng</dc:language>
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