Large Scale Medical Image Search via Unsupervised PCA Hashing

2013 ◽  
Author(s):  
Xiang Yu ◽  
Shaoting Zhang ◽  
Bo Liu ◽  
Lin Zhong ◽  
Dimitris N. Metaxas
Keyword(s):  
Medical Image ◽  
Large Scale ◽  
Image Search

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.

BSIFT: Toward Data-Independent Codebook for Large Scale Image Search

2015 ◽  
Vol 24 (3) ◽  
pp. 967-979 ◽  
Author(s):  
Wengang Zhou ◽  
Houqiang Li ◽  
Richang Hong ◽  
Yijuan Lu ◽  
Qi Tian
Keyword(s):  
Large Scale ◽  
Image Search

Medical Image Search

2012 ◽  
pp. 1-36
Author(s):  
Thomas Deserno
Keyword(s):  
Medical Image ◽  
Image Search

Hybrid-Indexing Multi-type Features for Large-Scale Image Search

2015 ◽  
pp. 446-460 ◽  
Author(s):  
Qingjun Luo ◽  
Shiliang Zhang ◽  
Tiejun Huang ◽  
Wen Gao ◽  
Qi Tian
Keyword(s):  
Large Scale ◽  
Image Search

scholarly journals Indexing of the CNN features for the large scale image search

2018 ◽  
Vol 77 (24) ◽  
pp. 32107-32131 ◽  
Author(s):  
Ruoyu Liu ◽  
Shikui Wei ◽  
Yao Zhao ◽  
Yi Yang
Keyword(s):  
Large Scale ◽  
Image Search

scholarly journals Resolving Clinicians’ Queries Across a Grid’s Infrastructure

2005 ◽  
Vol 44 (02) ◽  
pp. 149-153 ◽  
Author(s):  
F. Estrella ◽  
C. del Frate ◽  
T. Hauer ◽  
M. Odeh ◽  
D. Rogulin ◽  
...  
Keyword(s):  
Image Analysis ◽  
Data Storage ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Large Data ◽  
Computing Power ◽  
European Database ◽  
Order Of Magnitude ◽  
Network Speed

Summary Objectives: The past decade has witnessed order of magnitude increases in computing power, data storage capacity and network speed, giving birth to applications which may handle large data volumes of increased complexity, distributed over the internet. Methods: Medical image analysis is one of the areas for which this unique opportunity likely brings revolutionary advances both for the scientist’s research study and the clinician’s everyday work. Grids [1] computing promises to resolve many of the difficulties in facilitating medical image analysis to allow radiologists to collaborate without having to co-locate. Results: The EU-funded MammoGrid project [2] aims to investigate the feasibility of developing a Grid-enabled European database of mammograms and provide an information infrastructure which federates multiple mammogram databases. This will enable clinicians to develop new common, collaborative and co-operative approaches to the analysis of mammographic data. Conclusion: This paper focuses on one of the key requirements for large-scale distributed mammogram analysis: resolving queries across a grid-connected federation of images.

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