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Found 8 papers, 2 papers with code
Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology
Diagnoses from histopathology images rely on information from both high and low resolutions of Whole Slide Images.
Towards contrast-agnostic soft segmentation of the spinal cord
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis.
A stochastic household model for vector-borne diseases
We introduce a stochastic household model for vector-borne diseases, in particular as relevant to prominent vectors belonging to the Aedes genus and hence the Zika, chikungunya, and dengue viruses.
Application of Machine Learning to Sleep Stage Classification
Here we have shown the potential of various machine learning classifiers to automatically, accurately, and reliably classify vigilance states based on a single EEG reading and a single EMG reading.
Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality.
Estimation and Tracking of AP-diameter of the Inferior Vena Cava in Ultrasound Images Using a Novel Active Circle Algorithm
Medical research suggests that the anterior-posterior (AP)-diameter of the inferior vena cava (IVC) and its associated temporal variation as imaged by bedside ultrasound is useful in guiding fluid resuscitation of the critically-ill patient.
Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos
Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure.
Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
Image identification is one of the most challenging tasks in different areas of computer vision.
