AIMC Topic: Image Processing, Computer-Assisted

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Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder.

Biomedical engineering online
OBJECTIVE: In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, hig...

Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks.

Medical image analysis
Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearanc...

A Fungus Spores Dataset and a Convolutional Neural Network Based Approach for Fungus Detection.

IEEE transactions on nanobioscience
Fungus is enormously notorious for food, human health, and archives. Fungus sign and symptoms in medical science are non-specific and asymmetrical for extremely large areas resulting into a challenging task of fungal detection. Various traditional an...

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

Neural networks : the official journal of the International Neural Network Society
Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be...

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

NeuroImage
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuro...

Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning.

Photodiagnosis and photodynamic therapy
This study presents the analysis of hepatitis B virus (HBV) infection in human blood serum using Raman spectroscopy combined with pattern recognition technique. In total, 119 confirmed samples of HBV infected sera, collected from Pakistan Atomic Ener...

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.

Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Magnetic resonance in medicine
PURPOSE: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to asse...

Deep convolutional neural network for segmentation of knee joint anatomy.

Magnetic resonance in medicine
PURPOSE: To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tiss...

Structured radiology reporting on an institutional level-benefit or new administrative burden?

Annals of the New York Academy of Sciences
Significant technical advances have been made in radiology since the first discovery of X-rays. Diagnostic techniques have become more and more complex, workflows have been digitized, and data production has increased exponentially. However, the radi...