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Found 6 papers, 5 papers with code
Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs
In this paper, we propose our architecture that takes advantage of the fact that in hardware-limited environments, we often refrain from using the highest available input resolutions to guarantee a higher throughput.
ISLE: A Framework for Image Level Semantic Segmentation Ensemble
One key bottleneck of employing state-of-the-art semantic segmentation networks in the real world is the availability of training labels.
Exploring Weakly Supervised Semantic Segmentation Ensembles for Medical Imaging Systems
Reliable classification and detection of certain medical conditions, in images, with state-of-the-art semantic segmentation networks, require vast amounts of pixel-wise annotation.
SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters
Our new PerimeterFit module will be applied to pre-refine the CAM predictions before using the pixel-similarity-based network.
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation using Object Border Fitting for Medical Images
Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset.
FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features
Ultrasound imaging is one of the most prominent technologies to evaluate the growth, progression, and overall health of a fetus during its gestation.
