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Found 4 papers, 1 papers with code
Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer
Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2. 8% in women.
Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens
In this study, a radiomics approach was extended to optical fluorescence molecular imaging data for tissue classification, termed 'optomics'.
Resolution-Based Distillation for Efficient Histology Image Classification
Our results show that a combination of KD and self-supervision allows the student model to approach, and in some cases, surpass the classification accuracy of the teacher, while being much more efficient.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
It achieved a kappa score of 0. 525 and an agreement of 66. 6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0. 485 and agreement of 62. 7% on this test set.
