Search Results for author:
Found 17 papers, 6 papers with code
Fairness and bias correction in machine learning for depression prediction: results from four different study populations
A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations, which spreads through collected data.
medigan: A Python Library of Pretrained Generative Models for Enriched Data Access in Medical Imaging
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging.
Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries
This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection.
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation
In this paper, we propose Layer Ensembles, a novel uncertainty estimation method that uses a single network and requires only a single pass to estimate predictive uncertainty of a network.
Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification
Our experiments demonstrate that shared GANs notably increase the performance of both transformer and convolutional classification models and highlight this approach as a viable alternative to inter-centre data sharing.
Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study
In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting.
Domain generalization in deep learning for contrast-enhanced imaging
This calls for new tools for generalizing single-domain, single-center deep learning models across new unseen domains and clinical centers in contrast-enhanced imaging.
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning.
Style Curriculum Learning for Robust Medical Image Segmentation
A style transfer model with style fusion is employed to generate the curriculum samples.
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges.
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
We present the first federated learning study on the modality of cardiovascular magnetic resonance (CMR) and use four centers derived from subsets of the M\&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy (HCM).
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.
A radiomics approach to analyze cardiac alterations in hypertension
Hypertension is a medical condition that is well-established as a risk factor for many major diseases.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs.
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences.
