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Found 11 papers, 1 papers with code
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.
A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty
We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production, "clean" capital that generates no emissions but is initially less productive than dirty capital, and knowledge capital that increases with R\&D investment and leads to technological innovation in green sector productivity.
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
However, due to the insufficient amount of precise annotations, thigh muscle masks generated by deep learning approaches tend to misclassify intra-muscular fat (IMF) as muscle impacting the analysis of muscle volumetrics.
Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation
An accurate and robust quantitative measurement of brain volume change is paramount for translational research and clinical applications.
TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging
Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly used in neuroscience and neurological clinical research through a Diffusion Tensor Imaging (DTI) model.
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging the domain distribution gaps.
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications
Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.
Methods for Extracting Information from Messages from Primary Care Providers to Specialists
Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems.
Multiple Sclerosis Lesion Inpainting Using Non-Local Partial Convolutions
However, existing methods inpaint lesions based on texture information derived from local surrounding tissue, often leading to inconsistent inpainting and the generation of artifacts such as intensity discrepancy and blurriness.
