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Found 9 papers, 1 papers with code
Meta-Learning Initializations for Interactive Medical Image Registration
We present a meta-learning framework for interactive medical image registration.
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations.
Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration
Image registration is useful for quantifying morphological changes in longitudinal MR images from prostate cancer patients.
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning
The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after.
Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images
Prostate cancer (PCa) is one of the leading causes of death for men worldwide.
Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation
Organ morphology is a key indicator for prostate disease diagnosis and prognosis.
Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification
For images with unanimous consensus between observers, anatomical classification accuracy was 97. 2% and probe adjustment accuracy was 94. 9%.
Longitudinal Image Registration with Temporal-order and Subject-specificity Discrimination
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program.
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
Results show a median Dice similarity coefficient of 0. 90 with an interquartile range of 0. 08, with equivalent performance to the three operators (with a Williams' index of 1. 03), and outperforming a U-Net architecture without the need for batch normalisation.
