Weakly Supervised Segmentation and Classification of Alpha-Synuclein Aggregates in Brightfield Midbrain Images

A recent study has demonstrated the potential of vocal biomarkers in the early detection of Parkinson's disease (PD) by comparing the effectiveness of a Deep Neural Network (DNN) against traditional machine learning models. The research utilized two publicly available voice datasets to extract Mel-frequency cepstral coefficients (MFCCs) and assess model robustness through extensive validation strategies.

A recent study has utilized Artificial Intelligence and bioacoustic monitoring to determine the sex and age of the European lobster, Homarus gammarus, in Johnshaven, Scotland. By analyzing the bioacoustic emissions, researchers classified lobsters into juvenile/adult and male/female categories using advanced Deep Learning and Machine Learning models, enhancing understanding of this key species for fisheries and aquaculture.

A new approach to probabilistic robustness in deep learning, termed non-parametric probabilistic robustness (NPPR), has been proposed, which learns optimized perturbation distributions directly from data rather than relying on fixed distributions. This method aims to enhance the evaluation of model robustness under distributional uncertainty, addressing a significant limitation in existing probabilistic robustness frameworks.

A recent study has developed a deep learning model aimed at detecting ventriculomegaly in prenatal ultrasound images. This condition, characterized by dilated cerebral ventricles in the fetal brain, is crucial to diagnose early due to its association with increased risks for fetal aneuploidies and genetic syndromes. The model, known as the Ultrasound Self-Supervised Foundation Model with Masked Autoencoding (USF-MAE), was fine-tuned for binary classification of fetal brain ultrasound images.