Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)
Medical Imaging and Computer-Aided Diagnosis
Series: Lecture Notes in Electrical Engineering;
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Product details:
- Publisher Springer Nature Singapore
- Date of Publication 5 April 2026
- ISBN 9789819638659
- Binding Paperback
- See also 9789819638628
- No. of pages592 pages
- Size 235x155 mm
- Language English
- Illustrations XVI, 592 p. 238 illus., 215 illus. in color. 693
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Long description:
This book aims to provide a collaborative platform for leading technology minds to exchange insights, foster interdisciplinary dialogue, and propel advancements in both medical imaging and computer-aided diagnosis. As technology evolves, a plethora of state-of-the-art human imaging devices have made remarkable strides in the medical field, transforming diagnostic and treatment standards. Concurrently, there is a growing emphasis on extracting and deciphering extensive information from medical images, spurring the demand for innovative solutions. The fusion of digital image processing and computer vision technologies has paved the way for computer-aided diagnosis (CAD), a pivotal player in disease analysis. This conference extends a warm invitation to researchers, scholars, engineers, scientists, industry leaders, and graduate students active in these fields. Through diverse participation formats, including compelling poster presentations and enlightening oral sessions, attendees will gain profound insights into the intricate interplay between these realms. This book showcases the latest technological breakthroughs, forging valuable connections and envisioning future applications.
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.- 1 Oral Cancer Classification using a Hybrid Attention-aided Deep Learning Model.- 2 Deep Learning Frameworks for Histopathological Image Processing in Colorectal Cancer Diagnostics.- 3 Improving Knee Osteoarthritis Detection through a Multitask Learning Method from 2D MRI Slices.- 4 Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study, etc.
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