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Found 7 papers, 3 papers with code
Optical Image-to-Image Translation Using Denoising Diffusion Models: Heterogeneous Change Detection as a Use Case
We introduce an innovative deep learning-based method that uses a denoising diffusion-based model to translate low-resolution images to high-resolution ones from different optical sensors while preserving the contents and avoiding undesired artifacts.
Selective Prediction for Semantic Segmentation using Post-Hoc Confidence Estimation and Its Performance under Distribution Shift
Our findings show that post-hoc confidence estimators offer a cost-effective approach to reducing the impacts of distribution shift.
Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?
Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker.
How to Fix a Broken Confidence Estimator: Evaluating Post-hoc Methods for Selective Classification with Deep Neural Networks
Considering neural networks with softmax outputs, our goal is to identify the best confidence estimator that can be computed directly from the unnormalized logits.
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
Our experiments show that the choice of a BE method can compromise up to 15. 7% of the tumor segmentation performance.
Predição da Idade Cerebral a partir de Imagens de Ressonância Magnética utilizando Redes Neurais Convolucionais
In this work, deep learning techniques for brain age prediction from magnetic resonance images are investigated, aiming to assist in the identification of biomarkers of the natural aging process.
Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes
ICD coding from electronic clinical records is a manual, time-consuming and expensive process.
