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Found 6 papers, 0 papers with code
Active learning using adaptable task-based prioritisation
In this work, the task predictor is a segmentation network.
Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks
In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure.
Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy
To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images.
Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches
The main contributions of our study are a comparison of sparse and dense approach in pCLE image reconstruction, implementing trainable generalised NW kernel regression, and adaptation of synthetic data for training pCLE SR.
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
Our framework can exploit HR images, regardless of the domain where they are coming from, to transfer the quality of the HR images to the initial LR images.
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies.
