Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.
Highlights
Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor of the digestive system with few symptoms until the cancer is advanced [1]
Besides pancreatic histopathological image classification, another vital task in our study is comparing the performance of segmentation and classification in PDAC prediction
Due to the lack of publicly available datasets with pancreatic histopathological images, relatively little research has been done on the automatic detection of pancreatic adenocarcinoma
Summary
Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor of the digestive system with few symptoms until the cancer is advanced [1]. Some studies have attempted to automatically detect pancreatic cancers with WSIs based on feature extraction methods. For the WSI-level classification, 36 statistical features of these malignant probability heatmaps were harnessed to train a Light Gradient Boosting Machine (LightGBM) [36] model for the identification and diagnosis of PDAC. LightGBM is a gradient-boosting framework based on decision trees It is an efficient model with low memory usage, which is required for automatic histopathological image analysis in clinical practice. Besides pancreatic histopathological image classification, another vital task in our study is comparing the performance of segmentation and classification in PDAC prediction This would allow us to choose the most appropriate method for diagnosing a sample in a practical clinical application. We chose the dice coefficient [44] as the loss function
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