473 research outputs found
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Skeletal bone age assessment is a common clinical practice to diagnose
endocrine and metabolic disorders in child development. In this paper, we
describe a fully automated deep learning approach to the problem of bone age
assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017.
The dataset for this competition is consisted of 12.6k radiological images of
left hand labeled by the bone age and sex of patients. Our approach utilizes
several deep learning architectures: U-Net, ResNet-50, and custom VGG-style
neural networks trained end-to-end. We use images of whole hands as well as
specific parts of a hand for both training and inference. This approach allows
us to measure importance of specific hand bones for the automated bone age
analysis. We further evaluate performance of the method in the context of
skeletal development stages. Our approach outperforms other common methods for
bone age assessment.Comment: 14 pages, 9 figure
Feature Detection in Medical Images Using Deep Learning
This project explores the use of deep learning to predict age based on pediatric hand X-Rays. Data from the Radiological Society of North Americaās pediatric bone age challenge were used to train and evaluate a convolutional neural network. The project used InceptionV3, a CNN developed by Google, that was pre-trained on ImageNet, a popular online image dataset. Our fine-tuned version of InceptionV3 yielded an average error of less than 10 months between predicted and actual age. This project shows the effectiveness of deep learning in analyzing medical images and the potential for even greater improvements in the future. In addition to the technological and potential clinical benefits of these methods, this project will serve as a useful pedagogical tool for introducing the challenges and applications of deep learning to the Bryant community
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Sensor, Signal, and Imaging Informatics in 2017.
ObjectiveāTo summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.MethodsāPubMedĀ® and Web of ScienceĀ® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.ResultsāThe selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics
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