14 papers · ranked by Valyu relevance
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Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as…
Shota Okazaki, Yuichi Mine, Yuki Yoshimi, Yuko Iwamoto + 9 more
'Tzu-Yu Peng' 'Taku Nishimura' 'Tomoya Suehiro' 'Yuma Koizumi' 'Ryota Nomura' 'Kotaro Tanimoto' 'Naoya Kakimoto' 'Takeshi Murayama'] Transfer learning (TL) is an alternative approach to the full training of deep learning (DL) models from scratch and can transfer knowledge gained from large-scale data to solve different…
Xueyan Mei, Zelong Liu, Philip M. Robson, Brett Marinelli + 11 more
'Mingqian Huang' 'Amish Doshi' 'Adam Jacobi' 'Chendi Cao' 'Katherine E. Link' 'Thomas Yang' 'Ying Wang' 'Hayit Greenspan' 'Timothy Deyer' 'Zahi A. Fayad' 'Yang Yang'] Purpose To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical…
Maad Ebrahim, Mohammad Alsmirat, Mahmoud Al-Ayyoub
Over recent years, researchers and practitioners have encountered massive and continuous improvements in the computational resources available for their use. This allowed the use of resource-hungry Machine learning (ML) algorithms to become feasible and practical. Moreover, several advanced techniques are being used to…
Priyal Sobti, Anand Nayyar, Niharika, Preeti Nagrath + 1 more
Convolutional neural network is widely used to perform the task of image classification, including pretraining, followed by fine-tuning whereby features are adapted to perform the target task, on ImageNet. ImageNet is a large database consisting of 15 million images belonging to 22,000 categories. Images collected from…
Edward Vendrow, Ethan Schonfeld
The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiographs. The clinical writing of unstructured reports is time consuming and error-prone. An automated system would improve standardization…
Alvin Rajkomar, Sneha Lingam, Andrew G. Taylor, Michael Blum + 1 more
'John Mongan'] The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July…
Adam Stančić, Vedran Vyroubal, Vedran Slijepčević, Pier Luigi Mazzeo
This paper presents the evaluation of 36 convolutional neural network (CNN) models, which were trained on the same dataset (ImageNet). The aim of this research was to evaluate the performance of pre-trained models on the binary classification of images in a “real-world” application. The classification of wildlife…
Bagher Sistaninejhad, Habib Rasi, Parisa Nayeri
Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis…
Yufeng Zheng, Jun Huang, Tianwen Chen, Yang Ou + 1 more
The convolutional neural networks (CNNs) are a powerful tool of image classification that has been widely adopted in applications of automated scene segmentation and identification. However, the mechanisms underlying CNN image classification remain to be elucidated. In this study, we developed a new approach to address…
V. Sunanthini, J. Deny, E. Govinda Kumar, S. Vairaprakash + 4 more
Glaucoma is a disease where the optic nerve of the eyes is smashed up due to the building up of pressure inside the vision point. This has no symptoms at the initial stages, and hence, patients with this disease cannot identify them at the beginning stage. It is explained as if the pressure in the eye increases, then…
Rongguo Zhang, Chenhao Pei, Ji Shi, Shaokang Wang
In the field of deep learning for medical image analysis, training models from scratch are often used and sometimes, transfer learning from pretrained parameters on ImageNet models is also adopted. However, there is no universally accepted medical image dataset specifically designed for pretraining models currently.…
Wei Wang, Yiyang Hu, Ting Zou, Hongmei Liu + 2 more
Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of…
Marta Cullell-Dalmau, Sergio Noé, Marta Otero-Viñas, Ivan Meić + 1 more
'Carlo Manzo'] Deep learning architectures for the classification of images have shown outstanding results in a variety of disciplines, including dermatology. The expectations generated by deep learning for, e.g., image-based diagnosis have created the need for non-experts to become familiar with the working principles…