We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities.
Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices.
These results show the adequacy of JoB-VS for the challenging task of vessel segmentation in complete TOF-MRA images.
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.
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
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.
While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices.
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.
Deep learning methods have reached state-of-the-art performance in cardiac image segmentation.
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature.
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.
1 code implementation • 22 Jan 2021 • Vien Ngoc Dang, Francesco Galati, Rosa Cortese, Giuseppe Di Giacomo, Viola Marconetto, Prateek Mathur, Karim Lekadir, Marco Lorenzi, Ferran Prados, Maria A. Zuluaga
First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.
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.
Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance.
Accurate segmentation of the right ventricle (RV) is a crucial step in the assessment of the ventricular structure and function.
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.
To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required.
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.
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.
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.
no code implementations • 11 Oct 2017 • Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren
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.
1 code implementation • 3 Jul 2017 • Guotai Wang, Maria A. Zuluaga, Wenqi Li, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L. David, Jan Deprest, Sebastien Ourselin, Tom Vercauteren
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.
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.