Large Margin Aggregation of Local Estimates for Medical Image Classification

2014 ◽  
pp. 196-203 ◽  
Author(s):  
Yang Song ◽  
Weidong Cai ◽  
Heng Huang ◽  
Yun Zhou ◽  
David Dagan Feng ◽  
...  
Keyword(s):  
Image Classification ◽  
Medical Image ◽  
Large Margin ◽  
Medical Image Classification

scholarly journals Large Margin Local Estimate With Applications to Medical Image Classification

2015 ◽  
Vol 34 (6) ◽  
pp. 1362-1377 ◽  
Author(s):  
Yang Song ◽  
Weidong Cai ◽  
Heng Huang ◽  
Yun Zhou ◽  
David Dagan Feng ◽  
...  
Keyword(s):  
Image Classification ◽  
Medical Image ◽  
Local Estimate ◽  
Large Margin ◽  
Medical Image Classification

Non-Local Attention Learning for Medical Image Classification

2021 ◽  
Author(s):  
Yang Wen ◽  
Leiting Chen ◽  
Haisheng Chen ◽  
Ximan Tang ◽  
Yu Deng ◽  
...  
Keyword(s):  
Image Classification ◽  
Medical Image ◽  
Non Local ◽  
Medical Image Classification

scholarly journals TransMed: Transformers Advance Multi-Modal Medical Image Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1384
Author(s):  
Yin Dai ◽  
Yifan Gao ◽  
Fayu Liu
Keyword(s):  
Image Analysis ◽  
Image Classification ◽  
Long Range ◽  
Medical Image ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Lesion Detection ◽  
Tumor Segmentation ◽  
Medical Image Classification

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

Medical Image Classification Using Generalized Zero Shot Learning

2021 ◽  
Author(s):  
Dwarikanath Mahapatra ◽  
Behzad Bozorgtabar ◽  
Zongyuan Ge
Keyword(s):  
Image Classification ◽  
Medical Image ◽  
Medical Image Classification ◽  
Generalized Zero

Deep kNN for Medical Image Classification

2020 ◽  
pp. 127-136
Author(s):  
Jiaxin Zhuang ◽  
Jiabin Cai ◽  
Ruixuan Wang ◽  
Jianguo Zhang ◽  
Wei-Shi Zheng
Keyword(s):  
Image Classification ◽  
Medical Image ◽  
Medical Image Classification
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