--- title: Revolutionizing Cancer Diagnosis: How Deep Learning... description: The study introduces a self-supervised attention-based method for weakly supervised regression in digital pathology, which significantly improves biomarker... canonical: https://daily.dev/posts/revolutionizing-cancer-diagnosis-how-deep-learning-predicts-continuous-biomarkers-with-unprecedente-wlxhmn2bn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Revolutionizing Cancer Diagnosis: How Deep Learning Predicts Continuous Biomarkers with Unprecedented Accuracy | daily.dev og:description: The study introduces a self-supervised attention-based method for weakly supervised regression in digital pathology, which significantly improves biomarker... og:url: https://daily.dev/posts/revolutionizing-cancer-diagnosis-how-deep-learning-predicts-continuous-biomarkers-with-unprecedente-wlxhmn2bn og:image: https://api.daily.dev/og/posts/WLXhMN2bn.png og:image:alt: Revolutionizing Cancer Diagnosis: How Deep Learning Predicts Continuous Biomarkers with Unprecedented Accuracy og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Revolutionizing Cancer Diagnosis: How Deep Learning Predicts Continuous Biomarkers with Unprecedented Accuracy **[Machine Learning News](https://daily.dev/sources/mlnews)** · 4 min read · 0 upvotes · 0 comments ## Summary The study introduces a self-supervised attention-based method for weakly supervised regression in digital pathology, which significantly improves biomarker prediction accuracy. Regression-based deep learning outperforms classification-based deep learning in predicting continuous biomarkers. The CAMIL regression method is used to predict molecular biomarkers from pathology slides, showing superior performance in predicting HRD status. Regression models have potential in enhancing prognostic capabilities and refining predictions from histologic slides. ## Full article daily.dev links to this article rather than hosting it. 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