Scalable Feature Learning on Huge Knowledge Graphs for Downstream Machine Learning

A recent study has developed predictive and robust radiomics models aimed at assessing chemotherapy response in patients with high-grade serous ovarian carcinoma (HGSOC), a cancer typically diagnosed at an advanced stage. The research utilizes machine learning techniques to analyze computed tomography imaging data, enhancing the prediction of neoadjuvant chemotherapy response.

A recent study has introduced an anthropomorphic thresholded visual-search model observer, enhancing task-based image quality assessment by mimicking the human visual system. This model selectively processes high-salience features, improving discrimination performance and diagnostic accuracy while filtering out irrelevant variability.

Recent advancements in machine learning have led to the development of a new framework for the 3D reconstruction of clouds and tropical cyclones (TCs) from satellite imagery, addressing the challenges of accurate TC forecasting. This framework utilizes a pre-training and fine-tuning pipeline to convert 2D satellite images into detailed 3D cloud maps, significantly enhancing the understanding of TC structures.

A new standardized framework for automatic tuberculosis (TB) detection from cough audio and clinical data has been proposed, aiming to establish a reproducible baseline for TB prediction. This framework addresses inconsistencies in previous studies, which varied in datasets, cohort definitions, and evaluation metrics, making it challenging to compare results.