The study evaluates multi-instance learning frameworks with attention mechanisms in whole slide image classification and virtual staining, focusing on breast tumors. It compares the effectiveness of two approaches in weakly-supervised tumor detection and TP53 mutation classification. Identification of Regions of Interest (RoIs) for tumors proved effective at low resolutions (AUC > 0.95), unlike TP53 classifications (AUC < 0.71). Alterations in the attention layer resulted in new insights into morphological features rather than just increased accuracy, emphasizing the potential for interactive exploration of cancer etiologies.
In multi-instance learning frameworks with attention mechanisms for WSI classification, Regions of Interest (RoIs) for tumor detection achieved high identification accuracy, but TP53 mutation classification was more challenging.
Alterations to the attention layer in multi-instance learning led to insights into morphological features rather than improved accuracy, showcasing the need for greater exploration of variations in cancer etiology.
Higher resolution images (20x) aided in identifying Regions of Interest for mutation detection, suggesting an association between detail level and classification efficacy in breast cancer pathology.
The findings facilitate interactive exploration of recurring morphologies, encouraging novel hypotheses regarding their implications for cancer development and outcomes.
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