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Uncertainty Quantification in Deep Neural Networks for Cancer Grading

Ai Cancer Biology

AI Pathology Image Analysis Cancer Grading

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Ai Cancer Biology › AI Pathology Image Analysis Cancer Grading

Uncertainty Quantification in Deep Neural Networks for Cancer Grading

Research develops probabilistic frameworks and Bayesian methods that quantify model confidence and identify ambiguous cases in AI-based cancer grade predictions. This generates actionable uncertainty measures that guide clinical decision-making and flag challenging pathology cases requiring expert review.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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