Search Results for author:
Found 30 papers, 10 papers with code
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0. 81$\pm$0. 08 for CT, 0. 84$\pm$0. 09 for T1-weighted MRI, and 0. 88\pm0. 08 for previously unseen T2-weighted fat-saturated (FS) MRI.
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease
However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions.
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.
Comparison of retinal regions-of-interest imaged by OCT for the classification of intermediate AMD
In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used.
Computer-aided diagnosis and prediction in brain disorders
Regarding prediction, i. e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer.
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.
Ten years of image analysis and machine learning competitions in dementia
Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared.
Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review
The automatic segmentation of optic nerve head in OCT scans could further improve the current clinical management of glaucoma and other diseases.
Reproducible radiomics through automated machine learning validated on twelve clinical applications
In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application.
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.
Evaluating glioma growth predictions as a forward ranking problem
The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods.
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net.
Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
For prediction in MCI, performances decreased for both SVM (0. 665) and CNN (0. 702).
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
We here propose an analytical framework based on an unbiased learning strategy for group-wise registration that simultaneously registers images to the mean space of a group to obtain consistent segmentations.
Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach
The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status and mitotic index (MI).
Time efficiency analysis for undersampled quantitative MRI acquisitions
TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation.
WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
Accurate characterization of glioma is crucial for clinical decision making.
Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations
In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM).
Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework.
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.
Deep learning-based retinal vessel segmentation with cross-modal evaluation
The main goal is to analyse if a model trained in one of two modalities, Fundus Photography (FP) or Scanning Laser Ophthalmoscopy (SLO), is transferable to the other modality accurately.
Towards continuous learning for glioma segmentation with elastic weight consolidation
When finetuning a convolutional neural network (CNN) on data from a new domain, catastrophic forgetting will reduce performance on the original training data.
Multi-modal segmentation with missing MR sequences using pre-trained fusion networks
Missing data is a common problem in machine learning and in retrospective imaging research it is often encountered in the form of missing imaging modalities.
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions.
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space.
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI).
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief.
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions.
Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling
We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods.
A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling
To evaluate the accuracy, we performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease.
