Generating semantic templates to support the image annotation process

2005 ◽  
Author(s):  
J. Vompras ◽  
S. Conrad
Keyword(s):  
Image Annotation ◽  
Annotation Process

scholarly journals Deep learning for the estimation of water-levels using river cameras

2021 ◽  
Author(s):  
Remy Vandaele ◽  
Sarah L. Dance ◽  
Varun Ojha
Keyword(s):  
Image Annotation ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Flood Events ◽  
Segmentation Method ◽  
River Level ◽  
Image Series ◽  
Manual Intervention ◽  
Annotation Process ◽  
Topographic Surveys

Abstract. River level estimation is a critical task required for the understanding of flood events, and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river camera images to estimate the river levels, but currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We develop an approach using an automated water semantic segmentation method to ease the process of river level estimation from river camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the Severn and Avon rivers, UK (21 November–5 December 2012), we show that our algorithm is able to automate the annotation process with an accuracy greater than 91 %. Then, we apply our approach to year-long image series from the same cameras observing the Severn and Avon (from 1 June 2019 to 31 May 2020) and compare our results with nearby river-gauge measurements. Given the high correlation (Pearson's Correlation Coefficient > 0.94) between our results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations.

Multi-View Semi-Supervised Learning Based Image Annotation

2014 ◽  
Vol 1049-1050 ◽  
pp. 1486-1489
Author(s):  
Cheng Jian Sun ◽  
Song Hao Zhu ◽  
Zhe Shi
Keyword(s):  
Supervised Learning ◽  
Image Annotation ◽  
Image Database ◽  
General Purpose ◽  
Experimental Results ◽  
Multiple Views ◽  
Training Process ◽  
Annotation Process ◽  
Entropy Principle ◽  
Effectiveness And Efficiency

This paper proposes a novel multi-view semi-supervised learning scheme to improve the performance of image annotation by using multiple views of an image and leveraging the information contained in pseudo-labeled images. In the training process, labeled images are first adopted to train view-specific classifiers independently using uncorrelated and sufficient views, and each view-specific classifier is then iteratively re-trained using initial labeled samples and additional pseudo-labeled samples based on a measure of confidence. In the annotation process, each unlabeled image is assigned appropriate semantic annotations based on the maximum vote entropy principle and the correlationship between annotations with respect to the results of each optimally trained view-specific classifier. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed multi-view semi-supervised scheme.

Image Annotation with Bayesian Universal Background Model

2013 ◽  
Vol 39 (10) ◽  
pp. 1674
Author(s):  
Dong YANG ◽  
Xiu-Ling ZHOU ◽  
Ping GUO
Keyword(s):  
Image Annotation ◽  
Background Model ◽  
Universal Background Model

Digging for the truth: the case for active annotation in evaluating the credibility of online medical information (Preprint)

2020 ◽  
Author(s):  
Mikołaj Morzy ◽  
Bartłomiej Balcerzak ◽  
Adam Wierzbicki ◽  
Adam Wierzbicki
Keyword(s):  
Machine Learning ◽  
Medical Information ◽  
Representation Learning ◽  
Training Dataset ◽  
Highly Qualified ◽  
Human In The Loop ◽  
Annotation Process ◽  
Comprehensive Framework ◽  
Online Sources ◽  
The Web

BACKGROUND With the rapidly accelerating spread of dissemination of false medical information on the Web, the task of establishing the credibility of online sources of medical information becomes a pressing necessity. The sheer number of websites offering questionable medical information presented as reliable and actionable suggestions with possibly harmful effects poses an additional requirement for potential solutions, as they have to scale to the size of the problem. Machine learning is one such solution which, when properly deployed, can be an effective tool in fighting medical disinformation on the Web. OBJECTIVE We present a comprehensive framework for designing and curating of machine learning training datasets for online medical information credibility assessment. We show how the annotation process should be constructed and what pitfalls should be avoided. Our main objective is to provide researchers from medical and computer science communities with guidelines on how to construct datasets for machine learning models for various areas of medical information wars. METHODS The key component of our approach is the active annotation process. We begin by outlining the annotation protocol for the curation of high-quality training dataset, which then can be augmented and rapidly extended by employing the human-in-the-loop paradigm to machine learning training. To circumvent the cold start problem of insufficient gold standard annotations, we propose a pre-processing pipeline consisting of representation learning, clustering, and re-ranking of sentences for the acceleration of the training process and the optimization of human resources involved in the annotation. RESULTS We collect over 10 000 annotations of sentences related to selected subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, food allergy testing) for less than $7 000 employing 9 highly qualified annotators (certified medical professionals) and we release this dataset to the general public. We develop an active annotation framework for more efficient annotation of non-credible medical statements. The results of the qualitative analysis support our claims of the efficacy of the presented method. CONCLUSIONS A set of very diverse incentives is driving the widespread dissemination of medical disinformation on the Web. An effective strategy of countering this spread is to use machine learning for automatically establishing the credibility of online medical information. This, however, requires a thoughtful design of the training pipeline. In this paper we present a comprehensive framework of active annotation. In addition, we publish a large curated dataset of medical statements labelled as credible, non-credible, or neutral.

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

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