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. 2022 Jan 11;11:802205. doi: 10.3389/fonc.2021.802205

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Copyright © 2022 Feng, Huang, Liu, Chen, Zhou, Yu, Xue, Chen, Zhou, Kuang, Yang, Chen, Chen, Peng and Long

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Visualization of two patient samples for the three methods. (A) The TLRS-Gastric is based on the WSIs of GC. (B) The TLRS-ImageNet is based on the ImageNet dataset. (C) The TLRS-Lung is based on the WSIs of the lung. The positive and negative filters for the three methods are in the first row. In the second and third rows, the response heat map of the two patients’ negative and positive transfer learning features was noted. The red region represents a larger weight, which shows that the model focuses on the area of the CT image. GC, gastric cancer; PGL, primary gastric lymphoma; TLRS, transfer learning radiomics signature.