Models demonstrated strong performance in tumor detection tasks with AUC values exceeding 0.9. Comparisons revealed superior results against breast cancer datasets and similar performance with colon cancer datasets, although differing composition types were noted. Findings indicate that 5x magnification suffices for effective tumor identification while revealing models’ capabilities to discern tumors from unrelated artifacts. Analysis of attention scores suggested improved sparsity in attention compared to original architectures, leading to the conclusion that further magnification may not enhance classification performance significantly.
The models performed on par with the state-of-the-art for tumor detection tasks, achieving AUC values above 0.9, indicating strong classification capabilities in whole slide images.
Results showed better AUC than previously reported, supporting the adequacy of 5x magnification for tumor identification, revealing that models learned to differentiate tumors from unrelated factors.
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