GAN augmentation for multiclass image classification using hemorrhage detection as a case-study.

In recent years, the development and exploration of deeper and more complex deep learning models has been on the rise. However, the availability of large heterogeneous datasets to support efficient training of deep learning models is lacking. While linear image transformations for augmentation have been used traditionally, the recent development of generative adversarial networks (GANs) could theoretically allow us to generate an infinite amount of data from the real distribution to support deep learning model training.

High-frequency ultrasound in clinical dermatology: a review.

Background: Ultrasound was first introduced in clinical dermatology in 1979. Since that time, ultrasound technology has continued to develop along with its popularity and utility. Today, high-frequency ultrasound (HFUS), or ultrasound using a frequency of at least 10 megahertz (MHz), allows for high-resolution imaging of the skin from the stratum corneum to the deep fascia.

Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning.

Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided brachytherapy. However, most current studies are concentrated on single-needle detection by only using a small number of images with a needle, regardless of the massive database of US images without needles. In this paper, we propose a workflow for multi-needle detection by considering the images without needles as auxiliary.

MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model.

We develop a learning-based method to generate patient-specific pseudo computed tomography (CT) from routinely acquired magnetic resonance imaging (MRI) for potential MRI-based radiotherapy treatment planning. The proposed pseudo CT (PCT) synthesis method consists of a training stage and a synthesizing stage. During the training stage, patch-based features are extracted from MRIs.

Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.

Magnetic resonance imaging (MRI) provides a number of advantages over computed tomography (CT) for radiation therapy treatment planning; however, MRI lacks the key electron density information necessary for accurate dose calculation. We propose a dictionary-learning-based method to derive electron density information from MRIs. Specifically, we first partition a given MR image into a set of patches, for which we used a joint dictionary learning method to directly predict a CT patch as a structured output.