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Found 4 papers, 3 papers with code
Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes
Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available.
Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer
Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment.
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
We propose TMSS, an end-to-end Transformer based Multimodal network for Segmentation and Survival prediction that leverages the superiority of transformers that lies in their abilities to handle different modalities.
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources.
