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 …
Transfer learning for medical images analyses: A survey
The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …
science and also revitalized numerous fields where traditional machine learning methods …
Medklip: Medical knowledge enhanced language-image pre-training for x-ray diagnosis
In this paper, we consider enhancing medical visual-language pre-training (VLP) with
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …
domain-specific knowledge, by exploiting the paired image-text reports from the radiological …
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 …
A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
[HTML][HTML] Radiomics and artificial intelligence in lung cancer screening
F Binczyk, W Prazuch, P Bozek… - Translational lung cancer …, 2021 - ncbi.nlm.nih.gov
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76
million associated deaths reported in 2018. The key issue in the fight against this disease is …
million associated deaths reported in 2018. The key issue in the fight against this disease is …
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 …
How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis
Discriminating lung nodules as malignant or benign is still an underlying challenge. To
address this challenge, radiologists need computer aided diagnosis (CAD) systems which …
address this challenge, radiologists need computer aided diagnosis (CAD) systems which …