MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization. However, the existing literature has predominantly concentrated on identifying cellular irregularities with high magnification images and considering specific organs of squamous cell carcinoma origin. In contrast, relatively little attention has been given to recognizing structural abnormalities observable at low magnification images. In this paper, we consider squamous cell carcinoma histopathological images across different organs of origin captured at low magnification and these images are gathered from various centers to develop a robust model. We propose a novel deep neural network model (MultiSCCHisto-Net) that can detect squamous cell carcinoma of any organ irrespective of organ of origin. In addition, deep neural networks used for histopathological image analysis typically have many parameters, making them computationally expensive. To address this research gap, we incorporate knowledge distillation, which compresses knowledge from a complex teacher model (MultiSCCHisto-Net) into a smaller student model (MultiSCCHisto-Net-KD) while preserving performance and enhancing the generalization of the student model by learning from the teacher's intermediate layer features. Moreover, an explainable deep learning technique called gradient-weighted class activation mapping is incorporated to highlight the image areas that help to classify the sample into particular classes. This explainability significantly enhances our confidence in the proposed model outcomes. We evaluate the model's robustness using private multi-centric and publicly available datasets. Our results show that accuracy rates of 97% and 93% are achieved on private and public datasets, respectively, surpassing the performance of state-of-the-art models.