AIMC Topic: Deep Learning

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DeepSSM: A blueprint for image-to-shape deep learning models.

Medical image analysis
Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this re...

Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model.

BMC oral health
BACKGROUND: Accurate cephalometric analysis plays a vital role in the diagnosis and subsequent surgical planning in orthognathic and orthodontics treatment. However, manual digitization of anatomical landmarks in computed tomography (CT) is subject t...

Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of ParĂ¡, Brazil.

PloS one
Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperp...

Deep Learning-based Assessment of Facial Asymmetry Using U-Net Deep Convolutional Neural Network Algorithm.

The Journal of craniofacial surgery
OBJECTIVES: This study aimed to evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system to detect facial asymmetry on posteroanterior (PA) cephalograms and compare the results o...

Classification of rib fracture types from postmortem computed tomography images using deep learning.

Forensic science, medicine, and pathology
Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images in full detail can be a challenging task. With recent advances in artificial intelligence, an increasing number of systems have been developed to assi...

Deep learning in computed tomography super resolution using multi-modality data training.

Medical physics
BACKGROUND: One of the limitations in leveraging the potential of artificial intelligence in X-ray imaging is the limited availability of annotated training data. As X-ray and CT shares similar imaging physics, one could achieve cross-domain data sha...