摘要
Gastric cancer remains the third most common cause of cancer-related death. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, manual pathology examination is time-consuming and laborious. Computer-aided diagnosis (CAD) systems can assist pathologists in diagnosing pathological images, thus improving the efficiency of disease diagnosis. In this paper, we propose a two-branch network named LGFFN-GHI, which can classify histopathological images of gastric cancer into two categories: normal and abnormal. LGFFN-GHI consists of two parallel networks, ResNet18 and Pvt-Tiny, which extract local and global features of microscopic gastric tissue images, respectively. We propose a feature blending module (FFB) that fuses local and global features at the same resolution in a cross-attention manner. This enables ResNet18 to acquire the global features extracted by Pvt-Tiny, while enabling Pvt-Tiny to acquire the local features extracted by ResNet18. We conducted experiments on a novel publicly available subsize image database of gastric histopathology (GasHisSDB). The experimental results show that LGFFN-GHI achieves an accuracy of 96.814%, which is 2.388% and 3.918% better than the baseline methods ResNet18 and Pvt-Tiny, respectively. Our proposed network exhibits high classification performance, demonstrating its effectiveness and future potential for the gastric histopathology image classification (GHIC) task.
Gastric cancer remains the third most common cause of cancer-related death. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, manual pathology examination is time-consuming and laborious. Computer-aided diagnosis (CAD) systems can assist pathologists in diagnosing pathological images, thus improving the efficiency of disease diagnosis. In this paper, we propose a two-branch network named LGFFN-GHI, which can classify histopathological images of gastric cancer into two categories: normal and abnormal. LGFFN-GHI consists of two parallel networks, ResNet18 and Pvt-Tiny, which extract local and global features of microscopic gastric tissue images, respectively. We propose a feature blending module (FFB) that fuses local and global features at the same resolution in a cross-attention manner. This enables ResNet18 to acquire the global features extracted by Pvt-Tiny, while enabling Pvt-Tiny to acquire the local features extracted by ResNet18. We conducted experiments on a novel publicly available subsize image database of gastric histopathology (GasHisSDB). The experimental results show that LGFFN-GHI achieves an accuracy of 96.814%, which is 2.388% and 3.918% better than the baseline methods ResNet18 and Pvt-Tiny, respectively. Our proposed network exhibits high classification performance, demonstrating its effectiveness and future potential for the gastric histopathology image classification (GHIC) task.
