Chatterjee, Jyotirmoy

Chattoraj, Subhankar

Konik, Hubert

Nawn, Debaleena

Pal, Mousumi

Pratiher, Sawon

Rashumi, Ranjan

International audienceThis treatise proposes a dilated convolutional multi-layer perceptron (MLP)-mixer model (hereafter referred to as DiCoMLP-Mixer) for oral connective tissue (OCT) grading. The proposed DiCoMLP-Mixer framework comprehends dense multi-scale contextual feature representation by enabling exponential receptive field expansion without sacrificing resolution via dilated convolutions. The MLP-mixer backbone in the DiCoMLP-Mixer architecture leverages the attention mechanism of a transformer model for saliency abstraction in oral mucosa histopathological images (OMHIs) by the spatial encoding of OMHI patches. This study focuses on the oral mucosa's sub-epithelium region, which has considerable clinical significance but is understudied in the literature. DiCoMLP-Mixer's exhaustive experimental validation on two OCT layers of the sub-epithelium, namely papillary (L1) and reticular (L2) for oral cancer (OC) grading, classifying the three major oral potentially malignant disorders (OPMDs) and OC from healthy OCTs. The ablation study with the existing MLP-mixer model evinces enhanced OC screening performance, while Grad-CAM heatmaps exhibit DiCoMLP-Mixer's consistent clinical saliency for precise OC detection

A Multi-Scale Context Aggregation Enriched MLP-Mixer Model for Oral Cancer Screening from Oral Sub-epithelial Connective Tissues

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