Automated methods enable the analysis of PET/CT scans (left) to accurately predict tumor location and size (right). Credit: Nature Machine Intelligence (2024). DOI: 10.1038/s42256-024-00912-9 ...

Deep-learning algorithms provide a way to automate the analysis of connectomics data while still achieving high accuracy. How to make spatial maps of gene activity — down to the cellular level ...

Synthetic cannabinoids, a class of new psychoactive substances, have emerged as a significant public health and social ...

Please use one of the following formats to cite this article in your essay, paper or report: APA. Cuffari, Benedette. (2025, April 07). Using Deep Learning for Brain Imaging Data Analysis.

Deep learning, a branch of artificial intelligence, has been applied to medical image analysis. Among deep learning techniques, ... Deep learning algorithms such as DenseNet-169, ...

Deep Learning Pioneer Geoffrey Hinton Publishes New Deep Learning Algorithm This item in japanese Jan 10, 2023 2 min read ...

Clinical Photographic Images: Deep learning algorithms such as DenseNet-169, ResNet-101, and EfficientNet-b4 have been employed to analyze clinical photographs of oral lesions.

But, just as with deep learning, one pivotal moment suddenly placed it on the map. That moment came in October 2015, when DeepMind’s AlphaGo, trained with reinforcement learning, defeated the ...

Deep learning enables rapid detection of stroke-causing blockages. Assistance from the deep-learning algorithm improved the radiologists’ performance in detecting the cerebral aneurysms, increasing ...

Figuring out the ways in which algorithms and deep learning models are different is a good start if the goal is to reconcile them. Deep learning can’t generalize For starters, Blundell said ...