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Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification
We present a novel approach, Supervised Contrastive Vision Transformer (SupCon-ViT), for improving the classification of invasive ductal carcinoma in terms of accuracy and generalization by leveraging the inherent strengths and advantages of both transfer learning, i. e., pre-trained vision transformer, and supervised contrastive learning.
Highly Scalable Task Grouping for Deep Multi-Task Learning in Prediction of Epigenetic Events
There have been methods developed to address such negative transfer in other domains, such as computer vision.
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