Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …
learning models. However, their estimation is complex from both theoretical and …
A review of deep transfer learning and recent advancements
Deep learning has been the answer to many machine learning problems during the past two
decades. However, it comes with two significant constraints: dependency on extensive …
decades. However, it comes with two significant constraints: dependency on extensive …
COVID-19 detection through transfer learning using multimodal imaging data
Detecting COVID-19 early may help in devising an appropriate treatment plan and disease
containment decisions. In this study, we demonstrate how transfer learning from deep …
containment decisions. In this study, we demonstrate how transfer learning from deep …
Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization
Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one
of the top 10 leading causes of death. Accurate and early detection of TB is very important …
of the top 10 leading causes of death. Accurate and early detection of TB is very important …
A novel transfer learning based approach for pneumonia detection in chest X-ray images
Pneumonia is among the top diseases which cause most of the deaths all over the world.
Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the …
Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
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 …
Med3d: Transfer learning for 3d medical image analysis
The performance on deep learning is significantly affected by volume of training data.
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for …
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for …