Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer
The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort,n = 366/122). Deep learning features were extracted from diffusion-weighted imaging–quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural networ...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-world Data
AbstractThe current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, “ChestX-ray14,” which involved fourteen (14) disease labels, was randomly divided into the train, validation, and test sets with ratios of 0.7, 0.1, and 0.2. Two pretrained state-of-the-art networks, EfficientNet-b5 and CoAtNet-0-rw, were chosen as the backbones. After the fully-connected layer, a final layer of 14 sigmoid activation units was added to output each disease’s diagnosis. To achieve b...
Source: Journal of Digital Imaging - August 1, 2023 Category: Radiology Source Type: research
An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection
AbstractSolitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them are eagerly needed to avoid potentially morbid biopsies and surgical procedures. We explore herein the performance and interpretability of an MRI-radiomics variational quantum neural network (QNN) using a quantum-annealing mutual-information (MI) feature selection approach. We retrospectively included 423 patients with HGG and LBM (> 2 cm) who had a contrast-enhanced T1-weigh...
Source: Journal of Digital Imaging - July 28, 2023 Category: Radiology Source Type: research
A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images
AbstractThe human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small variations in typical and COVID-19 pneumonia, as well as the complexities involved in classifying infection regions. Currently, various deep learning (DL)-based methods are being introduced for the automatic detection of COVID-19 using computerized tomography (CT) scan images. In this paper, we propose the pelican optimization algorithm-based long short-term memory (POA-LSTM) me...
Source: Journal of Digital Imaging - July 25, 2023 Category: Radiology Source Type: research
ECTransNet: An Automatic Polyp Segmentation Network Based on Multi-scale Edge Complementary
In this study, we present a novel network architecture named ECTransNet to address the challenges in polyp segmentation. Specifically, we propose an edge complementary module that effectively fuses the differences between features with multiple resolutions. This enables the network to exchange features across different levels and results in a substantial improvement in the edge fineness of the polyp segmentation. Additionally, we utilize a feature aggregation decoder that leverages residual blocks to adaptively fuse high-order to low-order features. This strategy restores local edges in low-order features while preserving ...
Source: Journal of Digital Imaging - July 25, 2023 Category: Radiology Source Type: research
