Dr. Jing Ge Profile

Dr. Jing Ge

at Qilu Univ of Technology (Shandong Academy of Sciences)

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Proceedings Article | 25 March 2024 Paper

Pathological image segmentation of gastric cancer based on deep learning

Hehu Zhou, Jingshan Pan, Na Li, Jing Ge, Chengjun Zhou, Wantong Du

Proceedings Volume 13089, 1308909 (2024) https://doi.org/10.1117/12.3021445

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Gastric cancer is a serious health threat and pathological images are an important criterion in its diagnosis. These images can help doctors accurately identify cancerous regions and provide important evidence for clinical decision-making. Thanks to the remarkable achievements of deep learning technology in the field of image processing, an increasing number of superior image segmentation models have emerged. The Swin-Unet model has achieved great success in the field of image segmentation. However, when applied to the image segmentation of gastric cancer pathological section data, the segmentation boundary appears jagged. We have put forth two potential solutions. Initially, we devised an attention connection module to supplant the skip connections within the model, thereby enhancing the model’s predictive precision. Subsequently, we engineered a prognostic processing unit that inputs the model’s predictive outcomes and employs a Conditional Random Field (CRF) for further predictive computations. The enhanced model increases the DSC by 2% and decreases the HD by 17%. Additionally, the issue of jagged boundaries in prediction results has been better optimized. We conducted comparative and ablation experiments, and the results showed that our improved method increased the accuracy of the model’s predictions and reduced the jaggedness of the results at the boundary.

Proceedings Article | 6 May 2019 Paper

An efficient deep neural network for classification in esophageal cancer histopathological images (CNN+LSTM)

Jing Ge, Ying Xie, Di Wang

Proceedings Volume 11069, 110692W (2019) https://doi.org/10.1117/12.2524240

KEYWORDS: Image classification, Cancer, Data modeling, Convolution, Convolutional neural networks, Breast cancer, Tissues, Neural networks, Feature extraction, Image analysis

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Esophageal cancer is one of the leading causes of mortality worldwide. Early diagnostics are imperative of improving the chances of correct treatment and survival. Pathology examination requires time consuming scanning while often leads to a disagreement among pathologists. The computer-aided diagnosis systems are critical to improve the diagnostic efficiency and reduce the subjectivity and error of human. In this paper, a deep learning–based approach is proposed to classify the H&E stained histopathological images of esophageal cancer. The histopathological images are firstly normalized to correct the color variations during slide preparation. Then one image patch is cut into five pieces and gets five corresponded features via convolutional neural networks (CNNs). Subsequently, the five features of one image patch are fed into the Long Short-Term Memory (LSTM) model for further feature extraction and integrated as one for the last classification. The experiment results have demonstrated the proposed approach is effective to classify the esophageal histopathological images with the accuracy of 84.5% which outperforming 10 percent than GoogleNet.

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