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[HTML][HTML] Review of large vision models and visual prompt engineering
Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …
artificial general intelligence. As the development of large vision models progresses, the …
Artificial intelligence and machine learning in cancer imaging
An increasing array of tools is being developed using artificial intelligence (AI) and machine
learning (ML) for cancer imaging. The development of an optimal tool requires …
learning (ML) for cancer imaging. The development of an optimal tool requires …
Literature review: Efficient deep neural networks techniques for medical image analysis
Significant evolution in deep learning took place in 2010, when software developers started
using graphical processing units for general-purpose applications. From that date, the deep …
using graphical processing units for general-purpose applications. From that date, the deep …
Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …
including image acquisition, analysis and interpretation, and for the extraction of clinically …
Boxinst: High-performance instance segmentation with box annotations
We present a high-performance method that can achieve mask-level instance segmentation
with only bounding-box annotations for training. While this setting has been studied in the …
with only bounding-box annotations for training. While this setting has been studied in the …
[HTML][HTML] Volumetric memory network for interactive medical image segmentation
Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …
Scribbleprompt: fast and flexible interactive segmentation for any biomedical image
Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …
With enough labelled data, deep learning models can be trained to accurately automate …
Clustering propagation for universal medical image segmentation
Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …
interactive setups posing challenges in facilitating progress achieved in one task to another …
Weakly supervised segmentation of COVID19 infection with scribble annotation on CT images
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up
in tackling the COVID-19. Although the convolutional neural network has great potential to …
in tackling the COVID-19. Although the convolutional neural network has great potential to …