Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists' practices, we proposed using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial quadruple-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets.

A novel approach based on the probabilistic latent semantic analysis model (pLSA) for automatic musical genre classification is proposed in this paper. Unlike traditional usage, the pLSA is used to model musical genre instead of single music signal in the proposed approach. First, an unsupervised clustering algorithm is utilized to group temporal segments in music signals into several natural clusters. By this means, each music signal is decomposed into a bag of ldquoaudio wordsrdquo. Subsequently, the pLSA model of each musical genre is trained through a new iterative training procedure and well-known EM algorithm. This training procedure can iteratively update the pLSA model parameters by discriminatively computing weight of each training music signal and evidently improve the model's discriminative performance. Finally, these models can be used to classify new unseen music signals. Experiments on two commonly utilized databases show that our pLSA based approach can give promising results and the iterative learning procedure is effective.