Results 61 to 70 of about 549,786 (309)
Self-Supervised Feature Learning by Learning to Spot Artifacts
, 2018 We introduce a novel self-supervised learning method based on adversarial training. Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts, and then to extract features from its intermediate ...
Favaro, Paolo, Jenni, Simon
core +1 more source
Sharpness & Shift-Aware Self-Supervised Learning
, 2023 Self-supervised learning aims to extract meaningful features from unlabeled data for further downstream tasks. In this paper, we consider classification as a downstream task in phase 2 and develop rigorous theories to realize the factors that implicitly influence the general loss of this classification task.
Tran, Ngoc N. +5 more
openaire +2 more sources
Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
How Well Do Self-Supervised Models Transfer to Medical Imaging?
Journal of Imaging, 2022 Self-supervised learning approaches have seen success transferring between similar medical imaging datasets, however there has been no large scale attempt to compare the transferability of self-supervised models against each other on medical images.
Jonah Anton +8 more
doaj +1 more source
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
, 2019 Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone ...
Chen, Cen +8 more
core +1 more source
, 2020 This talk was presented for the Machine Learning Reading Group of the Wellcome Trust EPSRC for Interventional and Surgical Sciences of University College London, 11 February 2020.I gave an overview of self-supervised learning and some applications to computer vision and medical images.
openaire +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life‐threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data ...
Maike Stein +7 more
wiley +1 more source
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
, 2018 We introduce an unsupervised feature learning approach that embeds 3D shape information into a single-view image representation. The main idea is a self-supervised training objective that, given only a single 2D image, requires all unseen views of the ...
D Jayaraman +13 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective People with epilepsy (PWE) may experience cognitive deficits but fail to undergo formal evaluation. This study compares cognitive status between PWE and healthy controls in the West African Republic of Guinea. Methods A cross‐sectional, case–control study was conducted in sequential recruitment phases (July 2024–July 2025) at Ignace ...
Maya L. Mastick +14 more
wiley +1 more source
Colorization as a Proxy Task for Visual Understanding
, 2017 We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has been shown to be more promising for utilizing unlabeled data than other ...
Larsson, Gustav +2 more
core +1 more source