AI Medical Compendium Topic

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Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is...

An Efficient Method to Predict Pneumonia from Chest X-Rays Using Deep Learning Approach.

Studies in health technology and informatics
Pneumonia is a severe health problem causing millions of deaths every year. The aim of this study was to develop an advanced deep learning-based architecture to detect pneumonia using chest X-ray images. We utilized a convolutional neural network (CN...

Skeletal bone age assessments for young children based on regression convolutional neural networks.

Mathematical biosciences and engineering : MBE
Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back...

Model-free Cardiorespiratory Motion Prediction from X-ray Angiography Sequence with LSTM Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We present a novel model-free approach for cardiorespiratory motion prediction from X-ray angiography time series based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Cardiorespiratory motion prediction is defined as a problem of est...

Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possi...

Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Respiratory diseases account for a significant proportion of deaths and disabilities across the world. Chest X-ray (CXR) analysis remains a common diagnostic imaging modality for confirming intra-thoracic cardiopulmonary abnormalities. However, there...

[Automatic Segmentation of Anatomical Areas in X-ray Images Based on Fully Convolutional Networks].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: Medical image segmentation is a key step in medical image processing. An architecture of fully convolutional networks was proposed to realize automatic segmentation of anatomical areas in X-ray images.

Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image.

Journal of digital imaging
Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-c...

Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Osteosarcoma is the most common type of bone cancer. The primary means of osteosarcoma diagnosis is through evaluating plain x-rays. Using image analysis techniques, features that clinicians use to diagnose osteosarcoma can be quantified and studied ...

[Using Parallel Convolutional Neural Networks for Treatment Position Recognition in X-ray Images].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Treatment position recognition in medical images is a key technique in medical image processing. Due to the excellent performance of convolutional neural networks on features extraction and classification, an architecture of parallel convolutional ne...