SCRNet: a Collaborative Segmentation-Classification-reporting Framework Based on Dynamic Adaptation for Interpretable Thyroid Nodule Diagnosis

Computer-aided diagnosis holds significant promise for automatically screening thyroid nodules, thereby reducing missed and misdiagnosed cases in clinical practice. However, current deep learning approaches for thyroid nodules typically treat image segmentation and diagnostic classification as independent learning tasks, overlooking their intrinsic connection. This results in insufficient feature interaction between tasks, consequently limiting diagnostic accuracy. Furthermore, this task decoupling limits model interpretability, hindering clinical adoption. To address this, we propose SCRNet, an end-to-end interpretable multi-task collaborative learning framework that deeply integrates nodule segmentation, benign/malignant classification, and diagnostic report generation. Within the segmentation task, the prior knowledge guided module directs the model to focus on features critical to segmentation that align with clinicians' expertise. For classification, the graph reasoning module simulates the physician's diagnostic process by explicitly modelling relationships between regions. The multi-task coordination module explicitly fuses anatomical structure information derived from segmentation with raw image features.Finally, the collaborative features extracted through multi-task learning are decoded into structured diagnostic reports, with Bi-LSTM and context attention employed to enhance report accuracy and relevance. Experiments on the TR-TN3K dataset demonstrate that our method outperforms single-task models in image segmentation and diagnostic classification, achieving excellent performance (Dice: 0.87, accuracy: 0.799). For diagnostic classification and report generation, our method achieves high accuracy and clinical relevance, with strong matching of key diagnostic terms. The results indicate that this model can assist in clinical thyroid nodule diagnosis, reducing the risk of missed diagnoses and misdiagnoses, particularly for high-risk populations, thereby holding significant clinical value.