Researchers developed a dual-branch artificial intelligence framework combining deep learning and radiomics to detect Helicobacter pylori infection from gastroscopy images.
A retrospective deep learning framework analyzing routine hematoxylin and eosin stained whole-slide images has achieved a mean area under the ...
Researchers trained a deep neural network on clinical trial videos to predict colon capsule endoscopy positions across bowel segments over ...
A multicenter study demonstrates that integrating clinical, radiomics, habitat, and deep learning MRI features improves preoperative prediction of axillary lymph ...
A comparative study of nine U-Net-based deep learning configurations found U-Net with a VGG16 backbone achieved the best overall lung ...
A systematic comparison of nine CNN architectures on label-free multimodal microscopy images of colonic tissue found ResNet50 with partial fine-tuning ...
A hybrid ResNet50–VGG16 model with Coordinate Attention and focal loss reached a macro F1-score of 0.718 on the 22-class GastroVision ...
A new deep learning pipeline raises yellow rust classification accuracy on wheat leaves from 78 to 87 percent on the ...
A hybrid ResNet50 and Transformer model achieved 89.7 percent accuracy on the FER2013 facial emotion benchmark, with an ablation study ...
Pretrained convolutional neural networks classified biological sex from smartphone photographs of palatal rugae with up to 77 percent accuracy in ...
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