4,068 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Automatic mandibular canal detection using a deep convolutional neural network
The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.Peer reviewe
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
In the field of connectomics, neuroscientists seek to identify cortical
connectivity comprehensively. Neuronal boundary detection from the Electron
Microscopy (EM) images is often done to assist the automatic reconstruction of
neuronal circuit. But the segmentation of EM images is a challenging problem,
as it requires the detector to be able to detect both filament-like thin and
blob-like thick membrane, while suppressing the ambiguous intracellular
structure. In this paper, we propose multi-stage multi-recursive-input fully
convolutional networks to address this problem. The multiple recursive inputs
for one stage, i.e., the multiple side outputs with different receptive field
sizes learned from the lower stage, provide multi-scale contextual boundary
information for the consecutive learning. This design is
biologically-plausible, as it likes a human visual system to compare different
possible segmentation solutions to address the ambiguous boundary issue. Our
multi-stage networks are trained end-to-end. It achieves promising results on
two public available EM segmentation datasets, the mouse piriform cortex
dataset and the ISBI 2012 EM dataset.Comment: Accepted by ICCV201
- …