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Found 23 papers, 9 papers with code
Demographic Predictability in 3D CT Foundation Embeddings
Our results indicate that the embeddings effectively encoded age and sex information, with a linear regression model achieving a root mean square error (RMSE) of 3. 8 years for age prediction and a softmax regression model attaining an AUC of 0. 998 for sex classification.
Expanding the Horizon: Enabling Hybrid Quantum Transfer Learning for Long-Tailed Chest X-Ray Classification
We evaluated the performance of our Jax-based framework in terms of efficiency and performance for hybrid quantum transfer learning for long-tailed classification across 8, 14, and 19 disease labels using large-scale CXR datasets.
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.
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs.
Multi-environment lifelong deep reinforcement learning for medical imaging
Deep reinforcement learning(DRL) is increasingly being explored in medical imaging.
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.
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device.
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.
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging
Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting.
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 Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
We propose surgical aggregation, a FL method that uses selective aggregation to collaboratively train a global model using distributed, class-heterogeneous datasets.
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.
Multitask radiological modality invariant landmark localization using deep reinforcement learning
As a result, to develop a radiological decision support system, it would need to be equipped with potentially hundreds of deep learning models with each model trained for a specific task or organ.
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
A current clinical challenge is identifying limb girdle muscular dystrophy 2I(LGMD2I)tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle tissue.
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Therefore, we applied our multiparametric radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II and 16 grade IV).
Radiomic Synthesis Using Deep Convolutional Neural Networks
Radiomics is a rapidly growing field that deals with modeling the textural information present in the different tissues of interest for clinical decision support.
MPRAD: A Multiparametric Radiomics Framework
The use of radiomics for quantitative extraction of textural features from radiological imaging is increasing moving towards clinical decision support.
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results
For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images.
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results
On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map.
