Search Results for author:
Found 37 papers, 19 papers with code
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned
The study compared locally trained models with a federated model built across all institutions to evaluate FL's real-world feasibility.
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Deep learning (DL) has become the dominant approach for medical image segmentation, yet ensuring the reliability and clinical applicability of these models requires addressing key challenges such as annotation variability, calibration, and uncertainty estimation.
Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging
Instead, they advocate for the use of the Intraclass Correlation Coefficient (ICC) as a measure of stability for feature selection.
Federated nnU-Net for Privacy-Preserving Medical Image Segmentation
Federated learning is one of the approaches to train a segmentation model in a decentralized manner that helps preserve patient privacy.
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice.
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e. g., Fr\'echet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features.
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning
The teacher model further reinforces learning within this architecture through the imposition of consistency regularization constraints.
Democratizing AI in Africa: FL for Low-Resource Edge Devices
Africa faces significant challenges in healthcare delivery due to limited infrastructure and access to advanced medical technologies.
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
This work addresses these challenges exploring and quantifying the utility of privacy-preserving deep learning techniques, concretely, (i) differentially private stochastic gradient descent (DP-SGD) and (ii) fully synthetic training data generated by our proposed malignancy-conditioned generative adversarial network.
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations.
Mitigating annotation shift in cancer classification using single image generative models
However, the success of AI applications in this domain is restricted by the quantity and quality of available data, posing challenges due to limited and costly data annotation procedures that often lead to annotation shifts.
Debiasing Cardiac Imaging with Controlled Latent Diffusion Models
Notably, we conduct all our experiments using a single, consumer-level GPU to highlight the feasibility of our approach within resource-constrained environments.
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making.
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis.
USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR
We present USLR, a computational framework for longitudinal registration of brain MRI scans to estimate nonlinear image trajectories that are smooth across time, unbiased to any timepoint, and robust to imaging artefacts.
Revisiting Skin Tone Fairness in Dermatological Lesion Classification
Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form.
domain classification
Explainable artificial intelligence
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Fairness and bias correction in machine learning for depression prediction: results from four study populations
Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations.
medigan: a Python library of pretrained generative models for medical image synthesis
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging.
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five 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.
