Dive into the details of self-supervised learning for medical image analysis
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound
Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …
representations for transfer learning, leveraging large amounts of unlabelled data. This …
Joint self-supervised image-volume representation learning with intra-inter contrastive clustering
Collecting large-scale medical datasets with fully annotated samples for training of deep
networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in …
networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in …
A new algorithm for real-time detection of window opening area in residential buildings
Y Liu, WT Chong, YH Yau, D Han, M Qin, F Deng… - Building and …, 2023 - Elsevier
The variation in window opening areas by individuals can lead to significant differences in
air change rates, indirectly affecting indoor air quality and building energy consumption …
air change rates, indirectly affecting indoor air quality and building energy consumption …
Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection
T Gan, Z Jin, L Yu, X Liang, H Zhang, X Ye - Scientific Reports, 2023 - nature.com
Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the
use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated …
use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated …
Influence of intraoral scanners, operators, and data processing on dimensional accuracy of dental casts for unsupervised clinical machine learning: An in vitro …
Purpose. This study assessed the impact of intraoral scanner type, operator, and data
augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also …
augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also …
Dive into self-supervised learning for medical image analysis: Data, models and tasks
C Zhang, Y Gu - arXiv preprint arXiv:2209.12157, 2022 - arxiv.org
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabelled data. However, regarding a specific …
A survey of the impact of self-supervised pretraining for diagnostic tasks with radiological images
Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …
representations for transfer learning, leveraging large amounts of unlabelled data. This …
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Limited availability of labeled physiological data often prohibits the use of powerful
supervised deep learning models in the biomedical machine intelligence domain. We …
supervised deep learning models in the biomedical machine intelligence domain. We …
Enhanced Self-supervised Learning for Multi-modality MRI Segmentation and Classification: A Novel Approach Avoiding Model Collapse
Multi-modality magnetic resonance imaging (MRI) can provide complementary information
for computer-aided diagnosis. Traditional deep learning algorithms are suitable for …
for computer-aided diagnosis. Traditional deep learning algorithms are suitable for …