An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: May 11, 2022

Abstract

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.

Authors

  • Junseok Lee
    Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Seonjeong Kim
    Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Seongjin Park
    Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Jaesang Lee
    Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Korea.
  • Wonseop Hwang
    Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.
  • Seong Won Cho
    Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Korea.
  • Kyuho Lee
    Department of Materials Science and Engineering, Yonsei University, Seoul, 03722, Republic of Korea.
  • Sun Mi Kim
    Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gumi-dong.
  • Tae-Yeon Seong
    Department of Materials Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.
  • Cheolmin Park
    YU-KIST, Yonsei University, Seoul, 03722, Republic of Korea.
  • Suyoun Lee
    Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul 02792, Korea.
  • Hyunjung Yi
    Post-Silicon Semiconductor Institute, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea.

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