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Found 34 papers, 13 papers with code
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI
BACKGROUND AND PURPOSE: Deep learning has been demonstrated effective in many neuroimaging applications.
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields.
Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studies
Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols.
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns
The model first learns a transformation function from normal control (CN) domain to the patient (PT) domain with latent variables controlling transformation directions.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study
Our model can reduce diversity and improve the predictive and generalizing capability of the components.
Subtyping brain diseases from imaging data
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment.
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients
Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem.
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics
We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.
Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD).
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
Harmonization with Flow-based Causal Inference
Heterogeneity in medical data, e. g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize well.
Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies
We also demonstrate the improved reproducibility of the components while preserving age-related variation on a real dataset compiled from multiple sites.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
Extraction of Hierarchical Functional Connectivity Components in human brain using Adversarial Learning
The estimation of sparse hierarchical components reflecting patterns of the brain's functional connectivity from rsfMRI data can contribute to our understanding of the brain's functional organization, and can lead to biomarkers of diseases.
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation
We can also tackle situations where we do not have access to ground-truth labels on target data; we show how one can use auxiliary tasks for adaptation; these tasks employ covariates such as age, gender and race which are easy to obtain but nevertheless correlated to the main task.
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
Disentangling brain heterogeneity via semi-supervised deep-learning and MRI: dimensional representations of Alzheimer's Disease
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis.
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
A Deep Network for Joint Registration and Reconstruction of Images with Pathologies
They are therefore not designed for the registration of images with strong pathologies for example in the context of brain tumors, and traumatic brain injuries.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD).
Smile-GANs: Semi-supervised clustering via GANs for dissecting brain disease heterogeneity from medical images
The model was first trained using baseline MRIs from the ADNI2 database and then applied to longitudinal data from ADNI1 and BLSA.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images
We present a new deep learning based segmentation method, DeepMRSeg, that can be applied in a generic way to a variety of segmentation tasks.
Extraction of hierarchical functional connectivity components in human brain using resting-state fMRI
This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration
We present the formulation, discuss algorithmic features, describe the software package, and introduce an improved preconditioner for the reduced space Hessian to speed up the convergence of our solver.
PDE-constrained optimization in medical image analysis
We review related literature and give examples on the formulation, discretization, and numerical solution of PDE-constrained optimization problems for medical imaging.
Sparse Hierachical Extrapolated Parametric Methods for Cortical Data Analysis
In this paper, we demonstrate how the structure of the ubiquitous icosahedral meshes can be exploited by data factorization methods such as sparse dictionary learning, and we assess the optimization speed-up offered by extrapolation methods in this context.
