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Deep learning on medical image analysis
Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring
various diseases. Convolutional neural networks (CNNs) have become popular as they can …
various diseases. Convolutional neural networks (CNNs) have become popular as they can …
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Ce-net: Context encoder network for 2d medical image segmentation
Medical image segmentation is an important step in medical image analysis. With the rapid
development of a convolutional neural network in image processing, deep learning has …
development of a convolutional neural network in image processing, deep learning has …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT
Y ** a data-driven model for lung nodule segmentation
Accurate lung nodule segmentation from computed tomography (CT) images is of great
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …
From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities
Recent advancements in signal processing (SP) and machine learning, coupled with
electronic medical record kee** in hospitals and the availability of extensive sets of …
electronic medical record kee** in hospitals and the availability of extensive sets of …
Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches
Radiomics describes a broad set of computational methods that extract quantitative features
from radiographic images. The resulting features can be used to inform imaging diagnosis …
from radiographic images. The resulting features can be used to inform imaging diagnosis …
A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans
We introduce a new computer aided detection and diagnosis system for lung cancer
screening with low-dose CT scans that produces meaningful probability assessments. Our …
screening with low-dose CT scans that produces meaningful probability assessments. Our …