Generative Models Improve Radiomics Performance in Different Tasks and
Different Datasets: An Experimental Study
- URL: http://arxiv.org/abs/2109.02252v1
- Date: Mon, 6 Sep 2021 06:01:21 GMT
- Title: Generative Models Improve Radiomics Performance in Different Tasks and
Different Datasets: An Experimental Study
- Authors: Junhua Chen, Inigo Bermejo, Andre Dekker, Leonard Wee
- Abstract summary: Radiomics is an area of research focusing on high throughput feature extraction from medical images.
Generative models can improve the performance of low dose CT-based radiomics in different tasks.
- Score: 3.040206021972938
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Radiomics is an active area of research focusing on high throughput feature
extraction from medical images with a wide array of applications in clinical
practice, such as clinical decision support in oncology. However, noise in low
dose computed tomography (CT) scans can impair the accurate extraction of
radiomic features. In this article, we investigate the possibility of using
deep learning generative models to improve the performance of radiomics from
low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics
and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival
prediction and lung cancer diagnosis. We used encoder-decoder networks and
conditional generative adversarial networks (CGANs) trained in a previous study
as generative models to transform low dose CT images into full dose CT images.
Radiomic features extracted from the original and improved CT scans were used
to build two classifiers - a support vector machine (SVM) and a deep attention
based multiple instance learning model - for survival prediction and lung
cancer diagnosis respectively. Finally, we compared the performance of the
models derived from the original and improved CT scans. Encoder-decoder
networks and CGANs improved the area under the curve (AUC) of survival
prediction from 0.52 to 0.57 (p-value<0.01). On the other hand, Encoder-decoder
network and CGAN can improve the AUC of lung cancer diagnosis from 0.84 to 0.88
and 0.89 respectively (p-value<0.01). Moreover, there are no statistically
significant differences in improving AUC by using encoder-decoder network and
CGAN (p-value=0.34) when networks trained at 75 and 100 epochs. Generative
models can improve the performance of low dose CT-based radiomics in different
tasks. Hence, denoising using generative models seems to be a necessary
pre-processing step for calculating radiomic features from low dose CTs.
Related papers
- Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization.
We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM.
We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
arXiv Detail & Related papers (2024-07-02T19:30:25Z) - Rotational Augmented Noise2Inverse for Low-dose Computed Tomography
Reconstruction [83.73429628413773]
Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth.
We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN)
Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
arXiv Detail & Related papers (2023-12-19T22:40:51Z) - High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using
Semantic Diffusion Model [10.412300404240751]
Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years.
Deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the screening process.
However, developing robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations.
arXiv Detail & Related papers (2023-05-02T01:04:22Z) - A Novel Implementation of Machine Learning for the Efficient,
Explainable Diagnosis of COVID-19 from Chest CT [0.0]
The aim of this study was to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans.
The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958.
arXiv Detail & Related papers (2022-06-15T18:35:22Z) - Preservation of High Frequency Content for Deep Learning-Based Medical
Image Classification [74.84221280249876]
An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists.
We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
arXiv Detail & Related papers (2022-05-08T15:29:54Z) - Improving Classification Model Performance on Chest X-Rays through Lung
Segmentation [63.45024974079371]
We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations.
Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
arXiv Detail & Related papers (2022-02-22T15:24:06Z) - Self-Attention Generative Adversarial Network for Iterative
Reconstruction of CT Images [0.9208007322096533]
The aim of this study is to train a single neural network to reconstruct high-quality CT images from noisy or incomplete data.
The network includes a self-attention block to model long-range dependencies in the data.
Our approach is shown to have comparable overall performance to CIRCLE GAN, while outperforming the other two approaches.
arXiv Detail & Related papers (2021-12-23T19:20:38Z) - CNN Filter Learning from Drawn Markers for the Detection of Suggestive
Signs of COVID-19 in CT Images [58.720142291102135]
We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN)
For a few CT images, the user draws markers at representative normal and abnormal regions.
The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
arXiv Detail & Related papers (2021-11-16T15:03:42Z) - Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A
Simulation Study [4.7849095200575045]
Radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans.
In this article, we investigate the possibility of improving the radiomic features calculated on noisy CTs by using generative models for denoising.
The results show that denoising using encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) can improve the radiomic features calculated on noisy CTs.
arXiv Detail & Related papers (2021-04-30T15:18:57Z) - Residual Attention U-Net for Automated Multi-Class Segmentation of
COVID-19 Chest CT Images [46.844349956057776]
coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
There is still lack of studies on effectively quantifying the lung infection caused by COVID-19.
We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
arXiv Detail & Related papers (2020-04-12T16:24:59Z) - Experimenting with Convolutional Neural Network Architectures for the
automatic characterization of Solitary Pulmonary Nodules' malignancy rating [0.0]
Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures.
In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner.
More specifically, we intend to develop experimental Convolutional Neural Network (CNN) architectures and conduct experiments, by tuning their parameters, to investigate their behavior, and to define the optimal setup for the accurate classification.
arXiv Detail & Related papers (2020-03-15T11:46:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.