The Segment Anything Model (SAM) is a popular image processing tool for its segmentation accuracy, variety of input prompts, training capabilities, and efficient model design. However, its application to ultrasound…
Continue readingThe article introduces ConKeD, a novel deep learning approach for Retinal Image Registration (RIR). Unlike existing methods, ConKeD uses a multi-positive multi-negative contrastive learning strategy to utilize additional information from…
Continue readingSpatialVisVR is a virtual reality (VR) platform designed to help in comparing medical images. It’s adaptable for data privacy and can capture pathology slides in real time via mobile. The…
Continue readingThe article presents CaRe-CNN, a cascading refinement convolutional neural network (CNN) model for segmenting myocardial infarcts and microvascular obstructions in magnetic resonance imaging (MRI). The model is fully 3D and…
Continue readingThis article presents the development of LT-ViT, a Vision Transformer (ViT) for multi-label Chest X-ray (CXR) classification. Unlike previous ViTs, LT-ViT aggregates information from multiple scales, improving vision-only training for…
Continue readingThis research proposes a novel solution for medical image classification using Graph Neural Networks (GNNs). The approach uses Image Foresting Transform to segment images, which are then transformed into graph-structured…
Continue readingThe paper presents Fetal Ultrasound Semantic Clustering (FUSC), an unsupervised method that automates the clustering of ultrasound images into various fetal views. This approach eliminates the need for labor-intensive manual…
Continue readingELIXR, the “Embeddings for Language/Image-aligned X-Rays”, is a new approach to medical AI that addresses previous limitations. It does this by using a multimodal model that combines a vision encoder…
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