Data Stream Mining for Image Analysis Applications

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

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