Cellular spatial-semantic embedding for multi-label classification of cell clusters in thyroid fine needle aspiration biopsy whole slide images

Abstract

Multi-label classification of cell clusters is crucial for thyroid computer-aided diagnosis. The intricate spatial configurations and multifaceted semantic annotations inherent in thyroid fine-needle aspiration biopsy whole-slide images (FNAB-WSI) pose considerable obstacles to the precise multi-label classification of cell clusters. Considering the complex spatial structures and diverse label semantics in FNAB-WSI, we propose a multi-label classification method of cell clusters using cellular spatial-semantic embedding. This method effectively processes both spatial structure and multi-label semantic information. To address the challenge posed by limited training data for hard-to-classify categories, our method partially masks easily classifiable cells within the multi-label clusters. The preprocessed cell cluster images are then fed into a weighted down-sampling improved Convolutional vision Transformer (wCvT) encoder model to extract spatial features. The probability scores for each label are subsequently obtained through a multi-layer Transformer decoder that integrates both spatial features and label semantics, thus achieving accurate multi-label classification of the cell clusters. Experiments conducted on a self-built FNAB-WSI cell cluster dataset demonstrate an optimal classification accuracy of 90.26 % mAP, surpassing the highest comparable methods by 4.96 %. Moreover, the model employs a minimal number of parameters, with only 41.91 million parameters, achieving a tradeoff between accuracy and computational efficiency. This means that the proposed method could be utilized as a swift and precise computational intelligence aid for the clinical diagnosis of thyroid cancer.