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Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery
The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0. 997 and 0. 990, respectively, whereas the classical approach achieved only 0. 93 and 0. 82.
Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss
Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer representations.
Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching
Supervised contrastive loss (SCL) is a competitive and often superior alternative to the cross-entropy loss for classification.
