AIMC Topic: Deep Learning

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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.

Cell
The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common t...

Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.

Journal of personality and social psychology
We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic...

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography.

Radiographics : a review publication of the Radiological Society of North America, Inc
Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colonographic images to improve the visibility of polyps during virtual endoscopic "fly-through" reading. ...

Convolutional Neural Networks for ATC Classification.

Current pharmaceutical design
BACKGROUND: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organiz...

[Automated Classification of Calcification and Stent on Computed Tomography Coronary Angiography Using Deep Learning].

Nihon Hoshasen Gijutsu Gakkai zasshi
In computed tomography coronary angiography (CTCA), calcification and stent make it difficult to evaluate intravascular lumen. This is a cause of low positive-predictive value of coronary stenosis. Therefore, it is expected to develop a computer-aide...

Muscle Segmentation for Orthopedic Interventions.

Advances in experimental medicine and biology
Skeletal muscle segmentation techniques can help orthopedic interventions in various scenes. In this chapter, we describe two methods of skeletal muscle segmentation on 3D CT images. The first method is based on a computational anatomical model, and ...

Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images.

Advances in experimental medicine and biology
This chapter addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directl...

Deep Learning and Online Video: Advances in Transcription, Automated Indexing, and Manipulation.

Medical reference services quarterly
In recent years, the amount of video content created and uploaded to the Internet has grown exponentially. Video content has unique accessibility challenges: indexing, transcribing, and searching video has always been very labor intensive, and there ...