Titlebook: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis; First International Yipeng Hu,Roxane Licandro,Jordina Torrents B

FACET

Remote Intelligent Assisted Diagnosis System for Hepatic Echinococcosis the diagnosis of HE. However, HE mainly occurs in remote pastoral areas, where the economy is underdeveloped, medical conditions are backward, and ultrasound specialists are inadequate. Therefore, it is difficult for patients to receive timely and effective diagnosis and treatment. To address this

LEERY

Calibrated Bayesian Neural Networks to Estimate Gestational Age and Its Uncertainty on Fetal Brain Uoach is the use of a Bayesian Neural Network (BNN), which quantifies uncertainty of the estimated GA. Knowledge of estimated uncertainty is useful in clinical decision-making, and is especially important in ultrasound image analysis where image appearance and quality can naturally vary a lot. A furt

B-cell

Automatic Optic Nerve Sheath Measurement in Point-of-Care Ultrasound measurement of optic nerve sheath diameter (ONSD), using ultrasonography, has been shown to be a promising, non-invasive predictor of intracranial pressure (ICP). Unfortunately, the reproducibility and accuracy of this measure depends on the expertise of the sonologist- a requirement that limits th

遵循的規(guī)范

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to

最初

蛤肉

Guidewire Segmentation in 4D Ultrasound Sequences Using Recurrent Fully Convolutional Networksper addresses the problem of segmenting guidewires and other thin, flexible devices from 4D ultrasound image sequences acquired during minimally-invasive surgical interventions. We propose a deep learning method based on a recurrent fully convolutional network architecture whose design captures temp

人類

逗留

A Curriculum Learning Based Approach to Captioning Ultrasound Imagesontaining medical images and corresponding textual descriptions are relatively rare and hence, smaller-sized when compared to the datasets of natural images and their captions. This fact inspired us to develop an approach to train a captioning model suitable for small-sized medical data. Our dataset

Gum-Disease

single

Localizing 2D Ultrasound Probe from Ultrasound Image Sequences Using Deep Learning for Volume Reconsconvolutional neural network (CNN) to estimate the position of a 2D US probe only from US images. Our CNN model consists of two networks: feature extraction and motion estimation. We also introduce the consistency loss function to enforce. Through a set of experiments using US image sequence dataset