Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

Automated summary

Image analysis in histopathology is crucial for disease diagnosis, prognosis, and biomarker discovery, with precise classification being the ultimate goal. The TF-TS LSTM network is applied for classifying histopathological images, utilizing sequential time-frequency and time-space features for deep learning. Results show strong capability in accurate classification across various datasets, indicating the potential of this approach as an AI tool for robust classification of histopathological images.