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Found 15 papers, 4 papers with code
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation
The advancement of generative AI, particularly in medical imaging, confronts the trilemma of ensuring high fidelity, diversity, and efficiency in synthetic data generation.
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
Multiple local steps are key to communication-efficient federated learning.
A Cost-Aware Approach to Adversarial Robustness in Neural Networks
Considering the growing prominence of production-level AI and the threat of adversarial attacks that can evade a model at run-time, evaluating the robustness of models to these evasion attacks is of critical importance.
A Correlation- and Mean-Aware Loss Function and Benchmarking Framework to Improve GAN-based Tabular Data Synthesis
The proposed loss function demonstrates statistically significant improvements over existing methods in capturing the true data distribution, significantly enhancing the quality of synthetic data generated with GANs.
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)
The proposed approach uses survival models, worst-case examples, and a cost-aware analysis to precisely and accurately reject a particular model change during routine model training procedures rather than relying on real-world deployment, expensive formal verification methods, or accurate simulations of very complicated systems (\textit{e. g.}, digitally recreating every part of a car or a plane).
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation.
Reproducibility of the Methods in Medical Imaging with Deep Learning
We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but slightly adjusted guidelines on reproducibility and the quality of the public repositories.
A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation
Among different types of deep learning models, convolutional neural networks have been among the most successful and they have been used in many applications in medical imaging.
A Question-Centric Model for Visual Question Answering in Medical Imaging
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks.
Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation
In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs.
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging.
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord.
A general multiblock method for structured variable selection
However, for technical reasons, the variable selection offered by SGCCA was restricted to a covariance link between the blocks (i. e., with $\tau=1$).
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population.
