A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification
Abstract: Convolutional neural networks (CNNs) have been extensively studied for hyperspectral image classification (HSIC). However, CNNs are critically attributed to a large number of labeled ...
Abstract: The most predominant type of cancer is skin cancer, which is a major health issue, with millions of new cases being identified all over the world every year. Due to the variety and intricate ...
AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...
Researchers at the UCLA Samueli School of Engineering and CNSI (California NanoSystems Institute), led by Professor Aydogan ...
Subcortical ischemic vascular disease (SIVD), driven by cerebral small vessel disease, is commonly characterized by white matter hyperintensities and multiple lacunar infarcts, and a substantial ...
Researchers developed a hybrid UMAP-HDBSCAN-SVM machine learning workflow to rapidly classify low-loss STEM-EELS spectrum ...
Modern fluorescence microscopy can generate images of living cells as stunning to look at as they are informative to study. For techniques like fluorescence lifetime imaging microscopy (FLIM), those ...
[Notice] This list is not being maintained anymore because of the overwhelming amount of deep learning papers published every day since 2017. A curated list of the most cited deep learning papers ...
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