CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung
Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans
- URL: http://arxiv.org/abs/2110.08721v1
- Date: Sun, 17 Oct 2021 04:37:24 GMT
- Title: CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung
Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans
- Authors: Shahin Heidarian, Parnian Afshar, Anastasia Oikonomou, Konstantinos N.
Plataniotis, Arash Mohammadi
- Abstract summary: Lung Adenocarcinoma (LAUC) has recently been the most prevalent.
Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries.
The primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT.
In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs.
- Score: 36.093580055848186
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Lung cancer is the leading cause of mortality from cancer worldwide and has
various histologic types, among which Lung Adenocarcinoma (LAUC) has recently
been the most prevalent. Lung adenocarcinomas are classified as pre-invasive,
minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge
of the invasiveness of lung nodules leads to a proper treatment plan and
reduces the risk of unnecessary or late surgeries. Currently, the primary
imaging modality to assess and predict the invasiveness of LAUCs is the chest
CT. The results based on CT images, however, are subjective and suffer from a
low accuracy compared to the ground truth pathological reviews provided after
surgical resections. In this paper, a predictive transformer-based framework,
referred to as the "CAE-Transformer", is developed to classify LAUCs. The
CAE-Transformer utilizes a Convolutional Auto-Encoder (CAE) to automatically
extract informative features from CT slices, which are then fed to a modified
transformer model to capture global inter-slice relations. Experimental results
on our in-house dataset of 114 pathologically proven Sub-Solid Nodules (SSNs)
demonstrate the superiority of the CAE-Transformer over the
histogram/radiomics-based models and its deep learning-based counterparts,
achieving an accuracy of 87.73%, sensitivity of 88.67%, specificity of 86.33%,
and AUC of 0.913, using a 10-fold cross-validation.
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) - Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
Grading plays a vital role in breast cancer treatment planning.
The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs.
This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
arXiv Detail & Related papers (2024-05-13T15:48:26Z) - Double Integral Enhanced Zeroing Neural Network Optimized with ALSOA
fostered Lung Cancer Classification using CT Images [1.1510009152620668]
Lung cancer is one of the deadliest diseases and the leading cause of illness and death.
The proposed method attains 18.32%, 27.20%, and 34.32% higher accuracy analyzed with existing method.
arXiv Detail & Related papers (2023-12-05T10:53:35Z) - Variational Autoencoders for Feature Exploration and Malignancy
Prediction of Lung Lesions [0.0]
Lung cancer is responsible for 21% of cancer deaths in the UK.
Recent studies have demonstrated the capability of AI methods for accurate and early diagnosis of lung cancer from routine scans.
This study investigates the application Variational Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions.
arXiv Detail & Related papers (2023-11-27T11:12:33Z) - Classification of lung cancer subtypes on CT images with synthetic
pathological priors [41.75054301525535]
Cross-scale associations exist in the image patterns between the same case's CT images and its pathological images.
We propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on CT images.
arXiv Detail & Related papers (2023-08-09T02:04:05Z) - Penalized Deep Partially Linear Cox Models with Application to CT Scans
of Lung Cancer Patients [42.09584755334577]
Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies.
The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients.
We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
arXiv Detail & Related papers (2023-03-09T15:38:16Z) - Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung
Cancer Malignancy Prediction [14.7474816215111]
The paper proposes a novel hybrid discovery Radiomics framework.
It simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices.
It predicts Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement.
arXiv Detail & Related papers (2022-10-27T10:07:00Z) - Unsupervised Contrastive Learning based Transformer for Lung Nodule
Detection [6.693379403133435]
Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life.
Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context.
accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to variability in size, location, and appearance of lung nodules.
Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules.
arXiv Detail & Related papers (2022-04-30T01:19:00Z) - Generative Models Improve Radiomics Performance in Different Tasks and
Different Datasets: An Experimental Study [3.040206021972938]
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.
arXiv Detail & Related papers (2021-09-06T06:01:21Z) - Automated Quantification of CT Patterns Associated with COVID-19 from
Chest CT [48.785596536318884]
The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions.
The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities.
Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
arXiv Detail & Related papers (2020-04-02T21:49:14Z)
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.