Emergence of deep learning in knee osteoarthritis diagnosis
Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing
significant disability in patients worldwide. Manual diagnosis, segmentation, and …
significant disability in patients worldwide. Manual diagnosis, segmentation, and …
Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging
Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in
medical imaging. Materials and Methods Using two large publicly available radiology …
medical imaging. Materials and Methods Using two large publicly available radiology …
Predicting treatment response from longitudinal images using multi-task deep learning
Radiographic imaging is routinely used to evaluate treatment response in solid tumors.
Current imaging response metrics do not reliably predict the underlying biological response …
Current imaging response metrics do not reliably predict the underlying biological response …
A comparative analysis of automatic classification and grading methods for knee osteoarthritis focussing on X-ray images
Objective The purpose of present review paper is to introduce the reader to key directions of
manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from …
manual, semi-automatic and automatic knee osteoarthritis (OA) severity classification from …
Automated assessment and tracking of COVID-19 pulmonary disease severity on chest radiographs using convolutional siamese neural networks
Purpose To develop an automated measure of COVID-19 pulmonary disease severity on
chest radiographs for longitudinal disease tracking and outcome prediction. Materials and …
chest radiographs for longitudinal disease tracking and outcome prediction. Materials and …
A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification
There is a wide application of deep learning technique to unimodal medical image analysis
with significant classification accuracy performance observed. However, real-world …
with significant classification accuracy performance observed. However, real-world …
Chest imagenome dataset for clinical reasoning
Despite the progress in automatic detection of radiologic findings from chest X-ray (CXR)
images in recent years, a quantitative evaluation of the explainability of these models is …
images in recent years, a quantitative evaluation of the explainability of these models is …
A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law
B Qin, Y Wang, K Liu, S Jiang, Q Luo - Mechanical Systems and Signal …, 2023 - Elsevier
Due to issues such as limited variability in monitoring data across different tool wear
conditions and interference during the machining process, data-driven monitoring models …
conditions and interference during the machining process, data-driven monitoring models …
[HTML][HTML] DSRD-Net: Dual-stream residual dense network for semantic segmentation of instruments in robot-assisted surgery
T Mahmood, SW Cho, KR Park - Expert Systems with Applications, 2022 - Elsevier
In conventional robot-assisted minimally invasive procedures (RMIS), surgeons have narrow
visual and complex working spaces, along with specular reflection, blood, camera-lens …
visual and complex working spaces, along with specular reflection, blood, camera-lens …
Deep learning classification of breast cancer tissue from terahertz imaging through wavelet synchro-squeezed transformation and transfer learning
Terahertz imaging and spectroscopy is an exciting technology that has the potential to
provide insights in medical imaging. Prior research has leveraged statistical inference to …
provide insights in medical imaging. Prior research has leveraged statistical inference to …