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Found 9 papers, 3 papers with code
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach.
blob loss: instance imbalance aware loss functions for semantic segmentation
\emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters.
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
The test dataset will be used for model validation only and will not be released to the public.
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0. 07 when compared to full 3-D filters).
Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images
First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings
To approximate human quality ratings on scarce expert data, we train surrogate quality estimation models.
Approaching Peak Ground Truth
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such.
