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Found 24 papers, 12 papers with code
TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities.
Binary domain generalization for sparsifying binary neural networks
Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices.
JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images
These results show the adequacy of JoB-VS for the challenging task of vessel segmentation in complete TOF-MRA images.
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.
Sparsifying Binary Networks
Our experiments confirm that SBNNs can achieve high compression rates, without compromising generalization, while further reducing the operations of BNNs, making SBNNs a viable option for deploying DNNs in cheap, low-cost, limited-resources IoT devices and sensors.
Ranked #1 on
Sparse Learning and binarization
on CIFAR-100
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?
In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets.
AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics
While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices.
Improved optimization strategies for deep Multi-Task Networks
In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective functions.
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
Deep learning methods have reached state-of-the-art performance in cardiac image segmentation.
One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature.
Multi-Atlas Based Pathological Stratification of d-TGA Congenital Heart Disease
One of the main sources of error in multi-atlas segmentation propagation approaches comes from the use of atlas databases that are morphologically dissimilar to the target image.
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.
USAD: UnSupervised Anomaly Detection on Multivariate Time Series
Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.
Maximum Roaming Multi-Task Learning
Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.
Automatic Right Ventricle Segmentation using Multi-Label Fusion in Cardiac MRI
Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function.
Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting
Accurate travel products price forecasting is a highly desired feature that allows customers to take informed decisions about purchases, and companies to build and offer attractive tour packages.
Applications
Multi-objective Consensus Clustering Framework for Flight Search Recommendation
To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required.
Grey matter sublayer thickness estimation in themouse cerebellum
In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morphological parameters, to functional biomarkers, and represent an invaluable tool for longitudinal and cross-sectional clinical inference.
VTrails: Inferring Vessels with Geodesic Connectivity Trees
The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale.
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.
What is the distribution of the number of unique original items in a bootstrap sample?
The purpose of this report is to present the distribution of the number of unique original items in a bootstrap sample clearly and concisely, with a view to enabling other machine learning researchers to understand and control this quantity in existing and future resampling techniques.
