MambaBack is a hybrid Mamba-CNN model with Hilbert sampling and chunked inference that reports better performance than seven prior methods on five whole-slide image datasets.
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Global color moments and RGB/HSV histograms alone support binary benign-malignant classification at up to 89% accuracy with classical ML classifiers, substantially above random baselines.
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MambaBack: Bridging Local Features and Global Contexts in Whole Slide Image Analysis
MambaBack is a hybrid Mamba-CNN model with Hilbert sampling and chunked inference that reports better performance than seven prior methods on five whole-slide image datasets.
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Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification
Global color moments and RGB/HSV histograms alone support binary benign-malignant classification at up to 89% accuracy with classical ML classifiers, substantially above random baselines.