Special Issue "Data Stream Mining for Image Analysis Applications"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "AI in Imaging".

Deadline for manuscript submissions: 31 July 2023 | Viewed by 1981

Special Issue Editor

School of Computing and Digital Technology, Birmingham City University, Birmingham B47XJ, UK
Interests: data mining; stream mining; sensor networks/IOT; social media analysis; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mining data streams is an established subfield of artificial intelligence (AI) with over two decades of consistent contributions. Challenges posed by learning from fast online data (streaming data) have been addressed by numerous effective methods, especially for supervised learning (regression and classification). However, these methods were predominantly designed to deal with numerical data in a tabulated form. One of the main challenges posed by data stream mining is adapting to concept drifts (i.e., how do we update the model online in response to changes in the data distribution?). With the current AI revolution enabled by the remarkable success of deep learning, learning from unstructured data (image, audio, video, and text) has seen tremendous progress. However, the challenges posed by streaming data in deep learning are yet to be addressed. Early work on developing adaptive deep learning models to deal with concept drifts was published recently, but the area is still in its infancy. The plethora of methods in stream mining can play an important role in advancing many applications in image analysis. Medical, geological, biological and earth science applications are among the many areas that generate streaming data and can benefit from the detection of and adaptation to concept drifts.

This Special Issue aims at advancing image analysis applications through the adoption of new and existing data stream mining methods. Both research articles and comprehensive reviews are welcome. Topics of interest in image analysis include (but are not limited to):

  • Concept drift detection in deep neural networks.
  • Adapting to concept drifts in deep neural networks.
  • Applications of stream mining in medicine, geology, biology, earth science, and others.
  • Real-time out-of-distribution detection in deep neural networks.
  • Training deep neural networks on streaming data.
  • Fast inference in deep neural networks for streaming data.
  • Integration of online and offline learning for streaming data.
  • Novel streaming methods tailored to image analysis.
  • Adoption of existing streaming methods for image analysis.
  • Human-in-the-loop methods for streaming image analysis.
  • Explainable AI methods for streaming image analysis.

Prof. Dr. Mohamed Medhat Gaber
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks
J. Imaging 2023, 9(2), 26; https://doi.org/10.3390/jimaging9020026 - 24 Jan 2023
Cited by 2 | Viewed by 1564
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated [...] Read more.
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark. Full article
(This article belongs to the Special Issue Data Stream Mining for Image Analysis Applications)
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