Deep learning on medical image analysis

J Wang, S Wang, Y Zhang - CAAI Transactions on Intelligence …, 2024 - Wiley Online Library
Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring
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

V Cheplygina, M De Bruijne, JPW Pluim - Medical image analysis, 2019 - Elsevier
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 …

Ce-net: Context encoder network for 2d medical image segmentation

Z Gu, J Cheng, H Fu, K Zhou, H Hao… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …

A survey on deep learning in medical image analysis

G Litjens, T Kooi, BE Bejnordi, AAA Setio, F Ciompi… - Medical image …, 2017 - Elsevier
Deep learning algorithms, in particular convolutional networks, have rapidly become a
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
S Wang, M Zhou, Z Liu, Z Liu, D Gu, Y Zang… - Medical image …, 2017 - Elsevier
Accurate lung nodule segmentation from computed tomography (CT) images is of great
importance for image-driven lung cancer analysis. However, the heterogeneity of lung …

From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities

P Afshar, A Mohammadi, KN Plataniotis… - IEEE Signal …, 2019 - ieeexplore.ieee.org
Recent advancements in signal processing (SP) and machine learning, coupled with
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

M Zhou, J Scott, B Chaudhury, L Hall, D Goldgof… - American Journal of …, 2018 - ajnr.org
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 …

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 …