Supervised Machine Learning Based Medical Image Annotation and Retrieval in ImageCLEFmed 2005

2006 ◽  
pp. 692-701 ◽  
Author(s):  
Md. Mahmudur Rahman ◽  
Bipin C. Desai ◽  
Prabir Bhattacharya
Keyword(s):  
Machine Learning ◽  
Medical Image ◽  
Image Annotation ◽  
Supervised Machine Learning ◽  
Medical Image Annotation

scholarly journals Going to Extremes: Weakly Supervised Medical Image Segmentation

2021 ◽  
Vol 3 (2) ◽  
pp. 507-524
Author(s):  
Holger R. Roth ◽  
Dong Yang ◽  
Ziyue Xu ◽  
Xiaosong Wang ◽  
Daguang Xu
Keyword(s):  
Machine Learning ◽  
Medical Image ◽  
Expert Knowledge ◽  
Image Annotation ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Convolutional Network ◽  
Medical Image Annotation ◽  
Initial Segmentation ◽  
Speed Up

Medical image annotation is a major hurdle for developing precise and robust machine-learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points using the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. Through experimentation on several medical imaging datasets, we show that the predictions of the network can be refined using several rounds of training with the prediction from the same weakly annotated data. Further improvements are shown using the clicked points within a custom-designed loss and attention mechanism. Our approach has the potential to speed up the process of generating new training datasets for the development of new machine-learning and deep-learning-based models for, but not exclusively, medical image analysis.

Medical Image Annotation of Patients Based on SHOE

2005 ◽  
Vol 5 (4) ◽  
pp. 674-677
Author(s):  
Sadika Selka ◽  
Hafida Belbachir .
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Medical Image Annotation

scholarly journals A New Framework to Reduce Doctor’s Workload for Medical Image Annotation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 107097-107104 ◽  
Author(s):  
Yang Deng ◽  
Yao Sun ◽  
Yongpei Zhu ◽  
Yue Xu ◽  
Qianxi Yang ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Medical Image Annotation ◽  
New Framework

Editorial to the Special Issue on Medical Image Annotation in ImageCLEF 2007

2008 ◽  
Vol 29 (15) ◽  
pp. 1987 ◽  
Author(s):  
Thomas Deselaers ◽  
Henning Müller ◽  
Thomas M. Deserno
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Special Issue ◽  
Medical Image Annotation
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