AIMC Topic: Deep Learning

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Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.

Journal of digital imaging
We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs, with a spl...

Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography.

American journal of physiology. Heart and circulatory physiology
Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for ...

Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images.

Sensors (Basel, Switzerland)
Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extracti...

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System.

Sensors (Basel, Switzerland)
Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position b...

Harnessing clinical annotations to improve deep learning performance in prostate segmentation.

PloS one
PURPOSE: Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinicall...

Quality control stress test for deep learning-based diagnostic model in digital pathology.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising du...

Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Modern radiotherapy treatment planning is a complex and time-consuming process that requires the skills of experienced users to obtain quality plans. Since the early 2000s, the automation of this planning process has become an important research topi...

Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.

SLAS discovery : advancing life sciences R & D
Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While...

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance.

International journal of computer assisted radiology and surgery
PURPOSE: The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-st...