Results 31 to 40 of about 549,786 (309)
Self-supervised Learning for Spinal MRIs [PDF]
, 2017 A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs).
Jamaludin, A, Kadir, T, Zisserman, A
openaire +3 more sources
Self-supervised Learning: A Succinct Review
Archives of Computational Methods in Engineering, 2023 Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised learning learns from unlabeled data.
Veenu Rani +4 more
openaire +2 more sources
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction [PDF]
, 2019 In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
Bai, W. +8 more
core +4 more sources
Reduce the Difficulty of Incremental Learning With Self-Supervised Learning
IEEE Access, 2021 Incremental learning requires a learning model to learn new tasks without forgetting the learned tasks continuously. However, when a deep learning model learns new tasks, it will catastrophically forget tasks it has learned before.
Linting Guan, Yan Wu
doaj +1 more source
Review of Self-supervised Learning Methods in Field of ECG [PDF]
Jisuanji kexue yu tansuoDeep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically ...
HAN Han, HUANG Xunhua, CHANG Huihui, FAN Haoyi, CHEN Peng, CHEN Jijia
doaj +1 more source
Synergistic Self-supervised and Quantization Learning
, 2022 With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream tasks. However, we find that current SSL models suffer severe accuracy drops when performing low-bit quantization, prohibiting their deployment in resource-constrained ...
Yun-Hao Cao +4 more
openaire +2 more sources
Self-Distilled Self-supervised Representation Learning
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 WACV 23, 11 ...
Jang, Jiho +5 more
openaire +2 more sources
Adversarial Masking for Self-Supervised Learning
, 2022 We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial objective. The image encoder is trained to minimise the distance between representations of the original and that of a masked image.
Shi, Y +3 more
openaire +3 more sources
Self‐supervised learning with randomised layers for remote sensing
Electronics Letters, 2021 This letter presents a new self‐supervised learning approach based on randomised layers for remote sensing. Our method is basically based on the Tile2Vec approach, which is one of the state‐of‐the‐art self‐supervised learning approaches for remote ...
Heechul Jung, Taegyun Jeon
doaj +1 more source
Self-supervised learning methods and applications in medical imaging analysis: a survey [PDF]
PeerJ Computer Science, 2022 The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement.
Saeed Shurrab, Rehab Duwairi
doaj +2 more sources