[HTML][HTML] Review of large vision models and visual prompt engineering

J Wang, Z Liu, L Zhao, Z Wu, C Ma, S Yu, H Dai… - Meta-Radiology, 2023‏ - Elsevier
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 intelligence and machine learning in cancer imaging

DM Koh, N Papanikolaou, U Bick, R Illing… - Communications …, 2022‏ - nature.com
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 …

Literature review: Efficient deep neural networks techniques for medical image analysis

MA Abdou - Neural Computing and Applications, 2022‏ - Springer
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 …

Weakly supervised machine learning

Z Ren, S Wang, Y Zhang - CAAI Transactions on Intelligence …, 2023‏ - Wiley Online Library
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 …

A survey on active learning and human-in-the-loop deep learning for medical image analysis

S Budd, EC Robinson, B Kainz - Medical image analysis, 2021‏ - Elsevier
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 …

Boxinst: High-performance instance segmentation with box annotations

Z Tian, C Shen, X Wang… - Proceedings of the IEEE …, 2021‏ - openaccess.thecvf.com
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 …

[HTML][HTML] Volumetric memory network for interactive medical image segmentation

T Zhou, L Li, G Bredell, J Li, J Unkelbach… - Medical Image …, 2023‏ - Elsevier
Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …

Scribbleprompt: fast and flexible interactive segmentation for any biomedical image

HE Wong, M Rakic, J Guttag, AV Dalca - European Conference on …, 2024‏ - Springer
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 …

Clustering propagation for universal medical image segmentation

Y Ding, L Li, W Wang, Y Yang - Proceedings of the IEEE …, 2024‏ - openaccess.thecvf.com
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 …

Weakly supervised segmentation of COVID19 infection with scribble annotation on CT images

X Liu, Q Yuan, Y Gao, K He, S Wang, X Tang, J Tang… - Pattern recognition, 2022‏ - Elsevier
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 …