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Found 12 papers, 6 papers with code
Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS) MRIs from non-FS proton density sequences has the potential to accelerate acquisition of knee MRIs.
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL) models with limited translational utility.
Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations
The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient populations.
One Copy Is All You Need: Resource-Efficient Streaming of Medical Imaging Data at Scale
We propose MIST, an open-source framework to operationalize progressive resolution for streaming medical images at multiple resolutions from a single high-resolution copy.
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging
As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when streaming imaging data, leading to delays in patient care and increased cost.
Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery
We demonstrate that Text2Cohort can enable researchers to discover and curate cohorts on IDC with high levels of accuracy using natural language in a more intuitive and user-friendly way.
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels
Numerous large-scale chest x-ray datasets have spearheaded expert-level detection of abnormalities using deep learning.
Exploring Semantic Perturbations on Grover
With news and information being as easy to access as they currently are, it is more important than ever to ensure that people are not mislead by what they read.
Surgical Aggregation: Federated Class-Heterogeneous Learning
The release of numerous chest x-ray datasets has spearheaded the development of deep learning models with expert-level performance.
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations
Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications.
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease.
An Interpretable Generative Model for Handwritten Digit Image Synthesis
An interpretable generative model for handwritten digits synthesis is proposed in this work.
