Artificial intelligence and machine learning for medical imaging: A technology review
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …
of disruptive technical advances and impressive experimental results, notably in the field of …
[HTML][HTML] Self-supervised learning methods and applications in medical imaging analysis: A survey
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 …
collides with machine learning applications in the field of medical imaging analysis and …
[HTML][HTML] Self-supervised learning: A succinct review
Abstract Machine learning has made significant advances in the field of image processing.
The foundation of this success is supervised learning, which necessitates annotated labels …
The foundation of this success is supervised learning, which necessitates annotated labels …
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 …
Contrastive self-supervised learning from 100 million medical images with optional supervision
Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However …
assessment requires the creation of large sets of annotated training examples. However …
Intra-and inter-slice contrastive learning for point supervised oct fluid segmentation
OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …
Insect-foundation: A foundation model and large-scale 1m dataset for visual insect understanding
In precision agriculture the detection and recognition of insects play an essential role in the
ability of crops to grow healthy and produce a high-quality yield. The current machine vision …
ability of crops to grow healthy and produce a high-quality yield. The current machine vision …
[HTML][HTML] A review of predictive and contrastive self-supervised learning for medical images
WC Wang, E Ahn, D Feng, J Kim - Machine Intelligence Research, 2023 - Springer
Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …
progressing significantly on computer vision tasks. But, the application of deep learning in …
Generating and weighting semantically consistent sample pairs for ultrasound contrastive learning
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power
in extracting lesion-related features. Building such large and well-designed medical …
in extracting lesion-related features. Building such large and well-designed medical …
Micron-bert: Bert-based facial micro-expression recognition
Micro-expression recognition is one of the most challenging topics in affective computing. It
aims to recognize tiny facial movements difficult for humans to perceive in a brief period, ie …
aims to recognize tiny facial movements difficult for humans to perceive in a brief period, ie …