We propose an image processing method for ordering anterior chamber optical coherence tomography (OCT) images in a fully unsupervised manner. The method consists of three steps: Firstly we preprocess ...
An accurate medical image registration is crucial in a variety of neuroscience and clinical studies. In this paper, we proposed a new unsupervised learning network, DAVoxelMorph to improve the ...
Imaging technologies are ubiquitous in our daily lives, from smartphone cameras to medical imaging devices, helping us capture images and perceive objects. However, when faced with complex ...
Research finds using a large collection of simple, un-curated synthetic image generation programs to pretrain a computer vision model for image classification yields greater accuracy than employing ...
Researchers developed a hybrid UMAP-HDBSCAN-SVM machine learning workflow to rapidly classify low-loss STEM-EELS spectrum ...
Deep learning-based unsupervised morphological subtyping in histopathology images of gastric cancer.
The mutational pattern of homologous recombination (HR) related genes and its relevance to response to immunotherapy in gastric cancer. Comparison of Cohen’s kappa score among pathologists and DLS.
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