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1.	Chatterjee, Swarnadip, 1990-, et al. (författare) Investigating the Relevance of Contextual Information Towards Improving Deep CNN Based Oral Cancer Screening on Whole Slide Cytology Samples 2023 Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract Cases of Oral Cancer are increasing around the world. Oral Squamous Cell Carcinomas constitute the majority of all oral cancer cases and arise from the oral epithelium. Although this type of cancer is superficial and highly accessible to clinicians, it is often discovered late. To improve early detection in order to increase the chances of survival, we propose a Deep Convolutional Neural Network based framework on whole slide cytology images. In this ongoing work, we investigate the relevance of contextual information towards improving accuracy of classification of the pathological condition of cells from brush samples using only slide-level labels. For this, we consider nuclei centered patches of sizes 80 × 80, 160 × 160, 240 × 240, and 320 × 320 pixels and observe an increasing trend in the classification performances with respect to increasing patch sizes for three deep CNN architectures: ResNet50, DenseNet201 and SEResNet50.

Chatterjee, Swarnadip, 1990-, et al. (författare)
Investigating the Relevance of Contextual Information Towards Improving Deep CNN Based Oral Cancer Screening on Whole Slide Cytology Samples
2023
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Cases of Oral Cancer are increasing around the world. Oral Squamous Cell Carcinomas constitute the majority of all oral cancer cases and arise from the oral epithelium. Although this type of cancer is superficial and highly accessible to clinicians, it is often discovered late. To improve early detection in order to increase the chances of survival, we propose a Deep Convolutional Neural Network based framework on whole slide cytology images. In this ongoing work, we investigate the relevance of contextual information towards improving accuracy of classification of the pathological condition of cells from brush samples using only slide-level labels. For this, we consider nuclei centered patches of sizes 80 × 80, 160 × 160, 240 × 240, and 320 × 320 pixels and observe an increasing trend in the classification performances with respect to increasing patch sizes for three deep CNN architectures: ResNet50, DenseNet201 and SEResNet50.

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