Deep Learning Framework for Detecting Positive Lymph Nodes of Gastric Cancer on Histopathological Images

Gastric cancer is one of the most common cancers worldwide. The N staging of gastric cancer is a decisive factor in prognostic evaluation and decision-making of staged cancer treatment strategies. Pathologists generally judge the N staging of gastric cancer based on the number of lymph node metastasis on the histopathological WSIs. Tumor cells invading the lymph nodes are called lymph node metastasis, and we call the invaded lymph nodes positive lymph nodes. The mainstream method for judging positive lymph nodes is still by observing with the naked eye, which has problems such as heavy workload, time-consuming, and fatigue easily. Here, we propose a deep learning framework for identifying the number of lymph nodes and then determine whether each lymph node is benign or malignant. The framework consists of a lymph node detection network and a lymph node benign and malignant classification network. After training, the detection network and classification network achieved a recall rate of 94% and an accuracy rate of 95.7%, respectively. Furthermore, the framework maintains a 0.936 accuracy rate in the independent validation patient cases. The results demonstrated that our proposed framework can not only reduce pathological workload to a certain extent, but can also assist in identifying positive lymph nodes and determining the N staging of gastric cancer in patients.

• Publication year

  2021

• Venue

  International Conference on Biomedical Imaging, Signal Processing

• Publication date

  2021-10-29

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1145/3502803.3502806
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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