Richer fusion network for breast cancer classification based on multimodal data.
Background: Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists re...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/22/2021 Supplement 1
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149946452&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149946452 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/22/2021 Supplement 1 vid: 21 pid: 24147 pub: BioMed Central artinfo: ui: 149946452 149946452 NLM33888098 149946452 10.1186/s12911-020-01340-6 NLM33888098 149946452 ppf: 1 ppct: 14 formats: tig: atl: Richer fusion network for breast cancer classification based on multimodal data. aug: au: Yan, Rui Zhang, Fa Rao, Xiaosong Lv, Zhilong Li, Jintao Zhang, Lingling Liang, Shuang Li, Yilin Ren, Fei Zheng, Chunhou Liang, Jun affil: High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China sug: subj: Breast Neoplasms Breast Algorithms Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Background: Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification.Methods: In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data.Results: The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663 .Conclusions: We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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