期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 1, 页码 267-274出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.3023467
关键词
Global bank; encoder; decoder; CNN; RoIs
类别
资金
- National Key Research and Development Program of China [2017YFB1002504]
Diagnostic pathology is crucial for identifying carcinomas, and accurate quantification of pathological images can provide objective clues. The Global Bank (GLB) pathway has been proposed to guide the extraction of more RoI features, significantly improving performance and increasing the accuracy of quantitative results.
Diagnostic pathology is the foundation and gold standard for identifying carcinomas, and the accurate quantification of pathological images can provide objective clues for pathologists to make more convincing diagnosis. Recently, the encoder-decoder architectures (EDAs) of convolutional neural networks (CNNs) are widely used in the analysis of pathological images. Despite the rapid innovation of EDAs, we have conducted extensive experiments based on a variety of commonly used EDAs, and found them cannot handle the interference of complex background in pathological images, making the architectures unable to focus on the regions of interest (RoIs), thus making the quantitative results unreliable. Therefore, we proposed a pathway named GLobal Bank (GLB) to guide the encoder and the decoder to extract more features of RoIs rather than the complex background. Sufficient experiments have proved that the architecture remoulded by GLB can achieve significant performance improvement, and the quantitative results are more accurate.
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