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Found 15 papers, 8 papers with code
Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors
We evaluate our proposed method on the Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) dataset, where our method outperforms state-of-the-art methods in segmenting cardiac regions of interest in both short-axis and long-axis images.
Beyond the Known: Adversarial Autoencoders in Novelty Detection
The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution.
Crop and Couple: cardiac image segmentation using interlinked specialist networks
Diagnosis of cardiovascular disease using automated methods often relies on the critical task of cardiac image segmentation.
DEEPBEAS3D: Deep Learning and B-Spline Explicit Active Surfaces
Experimental results show that: 1) the proposed framework gives the user explicit control of the surface contour; 2) the perceived workload calculated via the NASA-TLX index was reduced by 30% compared to VOCAL; and 3) it required 7 0% (170 seconds) less user time than VOCAL (p< 0. 00001)
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation
In this work, we propose an adaptive multi-scale online likelihood network (MONet) that adaptively learns in a data-efficient online setting from both an initial automatic segmentation and user interactions providing corrections.
Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging
However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images.
ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans
This study aims to create a reliable and ef cient system for classifying AD using MRI by applying the deep Convolutional Neural Network (CNN).
Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma
It can be separated into two regions, intrameatal and extrameatal respectively corresponding to being inside or outside the inner ear canal.
FastGeodis: Fast Generalised Geodesic Distance Transform
The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware.
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation
Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes.
Federated Learning Versus Classical Machine Learning: A Convergence Comparison
Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server.
Evaluating the Communication Efficiency in Federated Learning Algorithms
These amounts of data are suitable for training different learning models.
PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks
We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems.
