Fast Filtering Techniques in Medical Image Classification and Retrieval

@inproceedings{Zhou2013FastFT,
  title={Fast Filtering Techniques in Medical Image Classification and Retrieval},
  author={Xin Zhou and Miaofei Han and Yanli Song and Qiang Li},
  booktitle={CLEF},
  year={2013}
}
This article presents the participation of the MIILab (Medical Image Information Laboratory) group in ImageCLEFmed2013. There are three types of tasks for ImageCLEFmed2013: modality classification, image retrieval and compound–image separation. Image modality classification and medical image retrieval are targeted according to MIILab’s research interest. The main goal is to perform a feasibility test on applying existing techniques on new applications, such as applying image denoising… CONTINUE READING

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Key Quantitative Results

  • For the image retrieval task, one baseline using SURFContext+BoF was submitted and the corresponding MAP (mean average precision) is 0.0086.

References

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Grid–based Medical Image Retrieval Using Local Features

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1 Excerpt

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