Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort.

Developing a deep-learning-based diagnostic model demands extensive labor for medical image labeling. Attempts to reduce the labor often lead to incomplete or inaccurate labeling, limiting the diagnostic performance of models. This paper (i) constructs an attention-guiding framework that enhances the diagnostic performance of jaw bone pathology by utilizing attention information with partially labeled data; (ii) introduces an additional loss to minimize the discrepancy between network attention and its label; (iii) introduces a trapezoid augmentation method to maximize the utility of minimally labeled data. The dataset includes 716 panoramic radiograph data for jaw bone lesions and normal cases collected and labeled by two radiologists from January 2019 to February 2021. Experiments show that guiding network attention with even 5% of attention-labeled data can enhance the diagnostic accuracy for pathology from 92.41 to 96.57%. Furthermore, ablation studies reveal that the proposed augmentation methods outperform prior preprocessing and augmentation combinations, achieving an accuracy of 99.17%. The results affirm the capability of the proposed framework in fine-grained diagnosis using minimally labeled data, offering a practical solution to the challenges of medical image analysis.

Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

Guaranteed cost controller design for discrete-time switching fuzzy systems.

With two level weighting functions, namely, crisp switching-region weighting functions and local fuzzy weighting functions, this paper introduces a discrete-time switching fuzzy system, which inherently contains the features of switched hybrid systems and Takagi-Sugeno (TS) fuzzy systems, and then, for this discrete-time switching fuzzy system, this paper proposes two new guaranteed cost state-feedback controllers minimizing an upper bound of state and input energy called LQ performance under all admissible grades of time-varying fuzzy weighting functions. The first one, associated with a piecewise quadratic Lyapunov function (PQLF), uses time-varying information on the switching-region weighting functions. The second one, associated with a new piece-wise fuzzy weighting-dependent Lyapunov function (PFWLF), uses time-varying information on the local fuzzy weighting functions as well as on the switching-region functions. Especially with a new special structure of the candidate of PFWLF, the PFWLF-based controller employs not only the current-time but also the one-step-past information on the time-varying local fuzzy weighting functions.