Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach.

Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification. However, the reliance on large labeled datasets poses challenges in the medical domain due to privacy issues and data silos. This study proposes a novel transfer learning approach integrated into a federated learning framework to solve the limitations of limited labeled data and data privacy in collaborative healthcare settings. For breast cancer classification, the mammography and MRO images were gathered from three different medical centers. Federated learning, an emerging privacy-preserving paradigm, empowers multiple medical institutions to jointly train the global model while maintaining data decentralization. Our proposed methodology capitalizes on the power of pre-trained ResNet, a deep neural network architecture, as a feature extractor. By fine-tuning the higher layers of ResNet using breast cancer datasets from diverse medical centers, we enable the model to learn specialized features relevant to different domains while leveraging the comprehensive image representations acquired from large-scale datasets like ImageNet. To overcome domain shift challenges caused by variations in data distributions across medical centers, we introduce domain adversarial training. The model learns to minimize the domain discrepancy while maximizing classification accuracy, facilitating the acquisition of domain-invariant features. We conducted extensive experiments on diverse breast cancer datasets obtained from multiple medical centers. Comparative analysis was performed to evaluate the proposed approach against traditional standalone training and federated learning without domain adaptation. When compared with traditional models, our proposed model showed a classification accuracy of 98.8% and a computational time of 12.22 s. The results showcase promising enhancements in classification accuracy and model generalization, underscoring the potential of our method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment.

Prevalence of metabolic syndrome and its association with atrial arrhythmias in patients with implanted permanent pacemaker for cardiac conduction abnormality.

The objective of the study was to find the prevalence of metabolic syndrome along with identifying the atrial arrhythmias, QTC interval, and coronary artery disease among these patients during follow-ups. Among 171 subjects who were implanted with permanent pacemakers, metabolic syndrome was present in 90 (52.6 %). Prevalence of Arrhythmias was 49 (28.7 %), atrial tachycardia (AT)/atrial fibrillation (AF) was seen in 29 (17 %) patients. Our study showed that there is a strong association between metabolic syndrome and atrial arrhythmias. Metabolic syndrome, age, coronary artery disease and Systolic blood pressure were good independent predictors of atrial arrhythmias among patients with pacemaker implantation.

Association of ABO blood groups with the severity of coronary artery disease in southern India population: A prospective cross-sectional study.

This study aimed to find an association between ABO blood groups with presence and severity of Coronary artery disease (CAD) among Indian population. 1500 patients undergoing elective coronary angiogram (CAG) at a tertiary care hospital in Karnataka were enrolled in the study. Baseline demographic data and the presence of cardiac comorbidities were documented. Data from baseline echocardiography and angiographic studies were compiled. The incidence of CAD was higher among patients with blood group A. Blood group A also showed a higher incidence of acute coronary syndrome (ACS), left ventricular dysfunction, triple vessel disease, and severe CAD among the patients who underwent CAG.