GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification.

A major challenge in applying deep learning to medical imaging is the paucity of annotated data. This study explores the use of synthetic images for data augmentation to address the challenge of limited annotated data in colonoscopy lesion classification. We demonstrate that synthetic colonoscopy images generated by Generative Adversarial Network (GAN) inversion can be used as training data to improve polyp classification performance by deep learning models.

Colonoscopy 3D video dataset with paired depth from 2D-3D registration.

Screening colonoscopy is an important clinical application for several 3D computer vision techniques, including depth estimation, surface reconstruction, and missing region detection. However, the development, evaluation, and comparison of these techniques in real colonoscopy videos remain largely qualitative due to the difficulty of acquiring ground truth data. In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy.

EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos.

Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth.

Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning.

While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.

DeepLSR: a deep learning approach for laser speckle reduction.

Speckle artifacts degrade image quality in virtually all modalities that utilize coherent energy, including optical coherence tomography, reflectance confocal microscopy, ultrasound, and widefield imaging with laser illumination. We present an adversarial deep learning framework for laser speckle reduction, called DeepLSR (https://durr.jhu.

An adaptive-coherence light source for hyperspectral, topographic, and flow-contrast imaging.

Colorectal cancer accounts for an estimated 8% of cancer deaths in the United States with a five-year survival rate of 55-75%. The early detection and removal of precancerous lesions is critical for reducing mortality, but subtle neoplastic growths, such as non-polypoid lesions, often go undetected during routine colonoscopy. Current approaches to flat or depressed lesion detection are ineffective due to the poor contrast of subtle features in white light endoscopy.

Transient Signals and Inattentional Blindness in a Multi-object Tracking Task.

Inattentional blindness is a failure to notice an unexpected event when attention is directed elsewhere. The current study examined participants' awareness of an unexpected object that maintained luminance contrast, switched the luminance once, or repetitively flashed. One hundred twenty participants performed a dynamic tracking task on a computer monitor for which they were instructed to count the number of movement deflections of an attended set of objects while ignoring other objects.