DBSIPA_22: Call for Chapters (Elsevier Book): 'Diagnostic biomedical signal and image processing applications with new deep learning methods' |
Submission link | https://easychair.org/conferences/?conf=dbsipa-22 |
Abstract registration deadline | January 31, 2022 |
Submission deadline | March 31, 2022 |
As a result of technological development, imaging and recording devices have become quite widespread, even in our pockets. Hundreds of thousands of images are recorded every day, even for specific purposes or just as a memory, using professional devices. In the era of big data, storing and retrieving these data becomes a very important problem. While the representation of images with fewer parameters is very important in terms of data storage efficiency, it is very important in image access in large datasets depending on the content. These tasks, which could be coped with using hand-crafted methods in the past, are now handled by deep learning methods. This special issue aims to bring together recent research works of Image Representation and Content-Based-Image Retrieval based on Deep Learning. We welcome researchers to discuss various aspects of these topics. We encourage researchers to innovate new solutions to the key problems in this emerging field. We solicit original works that have not been published nor under consideration in other publication venues.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference.
Please submit using;
https://easychair.org/my/conference?conf=dbsipa-22#
The following paper categories are welcome (but not limited to):
Chapter 1: Introduction to Deep Learning and Diagnosis in Medicine This chapter highlights needs, components, and current DL architectures, and the situation of literature.
Chapter 2: Skin lesion segmentation using novel CNN approach The chapter presents the introduction, skin lesion dataset, CNN method for segmentation of skin lesions
Chapter 3: 1D CNN based identification of Sleep disorders using EEG signals The chapter explains the fundamentals of the sleep stage that 1D CNN can use for the identification of sleep stages
Chapter 4: Emotion recognition using hybrid DL method. The EEG dataset and challenges of data acquisition, hybridization of DL.
Chapter 5: X-RAY specific 2D CNN model. Introduction a 2D CNN modal special for X-RAY images
Chapter 6: Classification of diseases from CT images using LSTM based CNN This chapter explains LSTM modules, CT dataset, and CT-related diseases.
Chapter 7: Tracking and detection mitosis from microscopy videos The chapter explains the fundamentals of mitosis and cell shapes, DL methods for microscopy videos
Chapter 8: Whole-slide histopathological image classification using patch-based CNN. Introduction of histopathological images, whole-slide and patch-based approaches, DL approaches for classification of whole-slide images.
Chapter 9: Motor-Imagery Tasks Classification in BCI Basic introduction of Motor-Imagery Tasks, features selection, DL-based solution, 1D CNN approaches.
Chapter 10: Machine Learning techniques for the development of smart health This chapter deals with some latest DL and CNN Approaches for smart healthcare.
Chapter 11: Volumetric medical image segmentation using 3D CNN The chapter explains the fundamentals of volumetric images, 3D CNN layers.
Chapter 12: Classification with unbalanced medical datasets. This chapter provides a novel solution for unbalanced medical datasets.
Chapter 13: A novel DL approach for ECG signal delineation This chapter proposes a novel approach for the delineation of ECG signals.
Chapter 14: EMG Signal Variations in Fatiguing Contractions of Muscles This chapter provides a solution and analysis for EMG signals using DL methods.
Contact
All questions about submissions should be emailed to
Prof. Kemal Polat, Abant İzzet Baysal University, kpolat@ibu.edu.tr
Assoc. Prof. Şaban Öztürk, Amasya University, saban.ozturk@amasya.edu.tr