Cancer prediction from few amounts of histology samples through self-attention based multi-routines cross-domains network.

Journal: Physics in medicine and biology
PMID: 37141901

Abstract

OBJECTIVE: Rapid and efficient analysis of cancer has become a focus of research. Artificial intelligence can use histopathological data to quickly determine the cancer situation, but still faces challenges. For example, the convolutional network is limited by the local receptive field, human histopathological information is precious and difficult to be collected in large quantities, and cross-domain data is hard to be used to learn histopathological features. In order to alleviate the above questions, we design a novel network, Self-attention based multi-routines cross-domains network (SMC-Net).

Authors

  • Jianqi Wang
    College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People's Republic of China.
  • Quan Zhang
    Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
  • Guohua Liu
    Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.

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