Figure 2 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
<p>Fusion status prediction from H&E images. <b>A,</b> Workflow for deep learning of normal tissue, FN-RMS, and FP-RMS. <b>B</b> and <b>C,</b> Representative (<b>B</b>) H&E images and (<b>C</b>) class activation maps as predic...
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2025
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| Özet: | <p>Fusion status prediction from H&E images. <b>A,</b> Workflow for deep learning of normal tissue, FN-RMS, and FP-RMS. <b>B</b> and <b>C,</b> Representative (<b>B</b>) H&E images and (<b>C</b>) class activation maps as predicted by the trained model. <b>D,</b> Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. <b>E,</b> Performance statistics for FP-RMS and FN-RMS prediction. Note: Normal tissues (<i>n</i> = 15) were classified with 100% sensitivity and 100% specificity and are included in performance metric calculations. <b>F,</b> Average ROC curve for FP-RMS vs. FN-RMS vs. normal predictions using holdout test data. <b>G,</b> Workflow for testing the trained algorithm against an independent RMS TMA dataset. <b>H,</b> Confusion matrix for predictions on a test dataset. Micro F1, Macro F1, and Matthew's correlation coefficient shown below. <b>I,</b> Performance statistics for FP-RMS and FN-RMS predictions on an independent dataset. <b>J,</b> Average ROC curve for FP-RMS vs. FN-RMS using an independent RMS tissue dataset.</p> |
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