Pattern Recognition 期刊征稿
期刊：Pattern Recognition
?？?/span>Special Issue on Deep Learning for Computer Aided Cancer Detection and Diagnosis with Medical Imaging
領域：人工智能
難度：★★★★
CCF分類：B類
影響因子：3.399
全文截稿：2017-08-15
網址：http://www.journals.elsevier.com/pattern-recognition/
-----------------------
Computer aided cancer detection and diagnosis (CAD) has made significant strides in the past 10 years, with the result that many successful CAD systems have been developed. However, the accuracy of these systems still requires significant improvement, so that the can meet the needs of real world diagnostic situations.. Recent progress in machine learning offers new prospects for computer aided cancer detection and diagnosis. A major recent development is the massive success resulting from the use of deep learning techniques, which has attracted attention from both the academic research and commercial application communities. Deep learning is the fastest-growing field in machine learning and is widespread uses in cancer detection and diagnosis. Recent research has demonstrated that deep learning can increase cancer detection accuracy significantly. Thus, deep learning techniques offer the promise not only of more accurate CAD systems for cancer detection and diagnosis, but may also revolutionize their design.
This special issue seeks high-quality original research papers on cancer detection and diagnosis in medical imaging and image processing. The topics of interest include, but are not limited to:
- Deep learning for cancer tissue classification
- Deep learning for cancer image segmentation
- Deep learning for cancer location
- Deep learning for cancer image retrieval
- Deep learning for high accuracy computer-aided detection/diagnosis systems
- Deep learning architecture for big cancer data
- GPU implementation of deep learning techniques for cancer detection/ diagnosis
- Real-time deep learning techniques for cancer detection/diagnosis
- Learning from multiple modalities of imaging data for cancer detection/diagnosis
- Deep learning for big image data analysis and its applications to cancer detection/diagnosis
