Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Heterogeneous multi-task learning with expert diversity
Predicting multiple heterogeneous biological and medical targets is a challenge for
traditional deep learning models. In contrast to single-task learning, in which a separate …
traditional deep learning models. In contrast to single-task learning, in which a separate …
Learning Representation for Multitask Learning Through Self-supervised Auxiliary Learning
Multi-task learning is a popular machine learning approach that enables simultaneous
learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the …
learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the …
[HTML][HTML] A transformer-based deep learning approach for fairly predicting post-liver transplant risk factors
Liver transplantation is a life-saving procedure for patients with end-stage liver disease.
There are two main challenges in liver transplant: finding the best matching patient for a …
There are two main challenges in liver transplant: finding the best matching patient for a …
UMT-net: A uniform multi-task network with adaptive task weighting
This article introduces a versatile multi-task learning framework (UMT-Net) and an adaptive
task weighting (ATW) training method, specifically designed for resource-constrained …
task weighting (ATW) training method, specifically designed for resource-constrained …
Multi-objective learning to predict pareto fronts using hypervolume maximization
Real-world problems are often multi-objective with decision-makers unable to specify a
priori which trade-off between the conflicting objectives is preferable. Intuitively, building …
priori which trade-off between the conflicting objectives is preferable. Intuitively, building …
Sequence signal prediction and reconstruction for multi-energy load forecasting in integrated energy systems: A bi-level multi-task learning method
Multi-energy load forecasting is the basis for the optimization and scheduling of integrated
energy systems (IES). An IES contains much heterogeneous energy with volatility and …
energy systems (IES). An IES contains much heterogeneous energy with volatility and …
Multi-stage ensemble with refinement for noisy labeled data classification
Deep neural networks (DNNs) have made remarkable progress in image classification.
However, since DNNs can memorize all the label information in the training dataset due to …
However, since DNNs can memorize all the label information in the training dataset due to …
[HTML][HTML] Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare
The digitization of healthcare systems has led to the proliferation of electronic health records
(EHRs), serving as comprehensive repositories of patient information. However, the vast …
(EHRs), serving as comprehensive repositories of patient information. However, the vast …
Bi-Fusion of Structure and Deformation at Multi-scale for Joint Segmentation and Registration
Medical image segmentation and registration are two fundamental and highly related tasks.
However, current works focus on the mutual promotion between the two at the loss function …
However, current works focus on the mutual promotion between the two at the loss function …