AIMC Topic: Deep Learning

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Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

Journal of applied clinical medical physics
PURPOSE: The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment.

Multi-task deep learning for medical image computing and analysis: A review.

Computers in biology and medicine
The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at le...

Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing.

Computers in biology and medicine
Pre-processing is widely applied in medical image analysis to remove the interference information. However, the existing pre-processing solutions mainly encounter two problems: (i) it is heavily relied on the assistance of clinical experts, making it...

Deep learning reveals what vocal bursts express in different cultures.

Nature human behaviour
Human social life is rich with sighs, chuckles, shrieks and other emotional vocalizations, called 'vocal bursts'. Nevertheless, the meaning of vocal bursts across cultures is only beginning to be understood. Here, we combined large-scale experimental...

The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts.

Applied microbiology and biotechnology
BACKGROUND: Monitoring jar fermenter-cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of the...

Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Proceedings of the National Academy of Sciences of the United States of America
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional...

Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging.

Sensors (Basel, Switzerland)
We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an...

Detecting Lombard Speech Using Deep Learning Approach.

Sensors (Basel, Switzerland)
Robust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the ...

Unusual Driver Behavior Detection in Videos Using Deep Learning Models.

Sensors (Basel, Switzerland)
Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to m...

An accurate deep learning model for wheezing in children using real world data.

Scientific reports
Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models req...