AIMC Topic: Deep Learning

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Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Medical image analysis
Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to complex organ shape, close distances to other organs, and large variations in size, shape, and filling status. The patient bowel is often not evacuat...

Deep learning networks reflect cytoarchitectonic features used in brain mapping.

Scientific reports
The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations ...

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting.

Journal of medical Internet research
BACKGROUND: Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as...

NSCR-Based DenseNet for Lung Tumor Recognition Using Chest CT Image.

BioMed research international
Nonnegative sparse representation has become a popular methodology in medical analysis and diagnosis in recent years. In order to resolve network degradation, higher dimensionality in feature extraction, data redundancy, and other issues faced when m...

Generative deep learning for macromolecular structure and dynamics.

Current opinion in structural biology
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular biology, where characterizing macromolecular structure and dynamics is...

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments.

Neuron
Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network ...

DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques.

NeuroImage
As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnet...

Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone.

Sensors (Basel, Switzerland)
Activity recognition can provide useful information about an older individual's activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphon...

Exploring a Structural Basis for Delayed Rod-Mediated Dark Adaptation in Age-Related Macular Degeneration Via Deep Learning.

Translational vision science & technology
PURPOSE: Delayed rod-mediated dark adaptation (RMDA) is a functional biomarker for incipient age-related macular degeneration (AMD). We used anatomically restricted spectral domain optical coherence tomography (SD-OCT) imaging data to localize de nov...