Hello World Deep Learning in Medical Imaging

Lakhani, Paras; Gray, Daniel L.; Pett, Carl R.; Nagy, Paul; Shih, George

doi:10.1007/s10278-018-0079-6

Cited by 92 publications

(51 citation statements)

References 9 publications

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“…A recent tutorial attempts to bridge this gap by providing a step by step implementation detail of applying DL to digital pathology images [8]. In [9], a high-level introduction to medical image segmentation task using deep learning is presented by providing the code. In general, most of the work using DL techniques use an open source model, where the code is made available on platforms such as github.…”

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confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This include application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval.

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confidence: 99%

Medical Image Analysis using Convolutional Neural Networks: A Review

et al. 2018

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“…Consequently, automatic methods based on deep neural networks have been tested for several purposes, which are as follows: classification, image registration, segmentation, lesion detection, image retrieval, image guided therapy, image generation, and enhancement . Most recently, radiomics and AI research have been advancing in the dental field, revealing the potential of these technologies to substantially improve clinical care …”

Section: Radiomics and DL Applications In Radiologymentioning

confidence: 99%

Radiomics and Machine Learning in Oral Healthcare

Leite

Vasconcelos

Willems

et al. 2020

Proteomics Clinical Apps

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The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.

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“…We applied the same hyper-parameters to both architectures; we used RMSprop optimizer with default parameters: learning rate = 0.001 and β = 0.9. The loss function we selected was the binary cross-entropy since this function better suits classification tasks with 2 classes [23]. All the convolutional layers were preceded by the zero or "same" padding to preserve the size of post convolution.…”

Section: Determining Cnn Architecture and Fine-tuningmentioning

confidence: 99%

An Investigation of a Convolution Neural Network Architecture for Detecting Distracted Pedestrians

Grishchenko¹,

Sayed²

2020

IJACSA

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The risk of pedestrian accidents has increased due to the distracted walking increase. The research in the autonomous vehicles industry aims to minimize this risk by enhancing the route planning to produce safer routes. Detecting distracted pedestrians plays a significant role in identifying safer routes and hence decreases pedestrian accident risk. Thus, this research aims to investigate how to use the convolutional neural networks for building an algorithm that significantly improves the accuracy of detecting distracted pedestrians based on gathered cues. Particularly, this research involves the analysis of pedestrian' images to identify distracted pedestrians who are not paying attention when crossing the road. This work tested three different architectures of convolutional neural networks. These architectures are Basic, Deep, and AlexNet. The performance of the three architectures was evaluated based on two datasets. The first is a new training dataset called SCIT and created by this work based on recorded videos of volunteers from Sheridan College Institute of Technology. The second is a public dataset called PETA, which was made up of images with various resolutions. The ConvNet model with the Deep architecture outperformed the Basic and AlexNet architectures in detecting distracted pedestrian.

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Hello World Deep Learning in Medical Imaging

Cited by 92 publications

References 9 publications

Medical Image Analysis using Convolutional Neural Networks: A Review

Medical Image Analysis using Convolutional Neural Networks: A Review

Radiomics and Machine Learning in Oral Healthcare

An Investigation of a Convolution Neural Network Architecture for Detecting Distracted Pedestrians

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