A review of deep learning on medical image analysis
Compared with common deep learning methods (eg, convolutional neural networks),
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …
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
The accurate identification of malignant lung nodules on chest CT is critical for the early
detection of lung cancer, which also offers patients the best chance of cure. Deep learning …
detection of lung cancer, which also offers patients the best chance of cure. Deep learning …
Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification
In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …
[HTML][HTML] Central focused convolutional neural networks: Developing 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 keeping in hospitals and the availability of extensive sets of …
electronic medical record keeping 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
O Ozdemir, RL Russell, AA Berlin - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …