T2TCAN : A Twin‐Token Transformer for Interpretable Grading of Invasive Ductal Carcinoma

ABSTRACT Accurate grading of invasive ductal carcinoma (IDC) remains hard even for the trained pathologist. The patient's therapeutic schedule depends on the observed grade, emphasizing the importance of accurate tissue grading. We propose a novel architecture called Twin Tokens Talking‐heads Class Attention Net (T2TCAN), designed to grade IDC images. Based on the transformer architecture, this design enhances the model's ability to capture local and global tissue features. This model was trained on the PathoIDCG dataset, outperforming the existing methods in terms of standard metrics. Further, introducing class and twin tokens later in the model, along with the talking‐heads class attention and per‐channel scaling, made the T2TCAN model efficient at distinguishing the tissue grades. Our model outperformed conventional CNN‐based IDC grading approaches. We report the highest 98.82% accuracy, AUC (0.99), precision (98.83%), recall (98.80%), and F1‐score (98.81%) on the PathoIDCG dataset. We plotted class attention maps for the TCT layers to provide insights into the model's decision‐making process. These attention maps are also helpful for pathologists to understand the discriminative features that significantly contribute to prognostic scoring. Further, our model also performed well on a separate IDC grading dataset, producing state‐of‐the‐art results and thus proving its efficacy. The designed model claims its adaptability and suitability for integration into the healthcare workflow for IDC grading.