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Found 7 papers, 0 papers with code
Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers
To the best of our knowledge, it is the first time to use keypoint regression for RECIST diameter prediction.
PieTrack: An MOT solution based on synthetic data training and self-supervised domain adaptation
In order to cope with the increasing demand for labeling data and privacy issues with human detection, synthetic data has been used as a substitute and showing promising results in human detection and tracking tasks.
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images
In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images.
Learning Numerical Observers using Unsupervised Domain Adaptation
In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones.
Automatic microscopic cell counting by use of deeply-supervised density regression model
Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired.
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression
A domain adaptation model (DAM) is built to map experimental images (the target domain) to the feature space of the source domain.
Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images
Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images.
