Abstract
Mitosis segmentation plays a vital role in early cancer detection, facilitating the accurate identification of dividing cells in histopathology images. Manual mitosis counting is time-consuming and subjective, prompting the need for automated approaches to improve efficiency and accuracy. In this study, we have developed a transformer-based U-Net model that combines the effectiveness of transformers which were originally designed for natural language processing (NLP) tasks, with the efficiency of the U-Net architecture to effectively capture both high-level and low-level features in histopathology images. We train and evaluate the model on the GZMH dataset and compare its performance against other deep models such as U-Net, U-Net++ and Mobilenetv2-based U-Net. The results demonstrate that transformer-based U-Net model is better in terms of accuracy, recall, precision, F1-score and Dice coefficient. This study represents a significant advancement in mitosis segmentation, contributing to improved cancer detection and prognosis.
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Kanadath, A., Jothi, J.A.A., Urolagin, S. (2024). Enhancing Mitotic Cell Segmentation: A Transformer Based U-Net Approach. In: Muthalagu, R., P S, T., Pawar, P.M., R, E., Prasad, N.R., Fiorentino, M. (eds) Computational Intelligence and Network Systems. CINS 2023. Communications in Computer and Information Science, vol 1978. Springer, Cham. https://doi.org/10.1007/978-3-031-48984-6_11
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