Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring

Deep learning models achieve strong performance in chest radiograph (CXR) interpretation, yet fairness and reliability concerns persist. Models often show uneven accuracy across patient subgroups, leading to hidden failures not reflected in aggregate metrics. Existing error detection approaches -- based on confidence calibration or out-of-distribution (OOD) detection -- struggle with subtle within-distribution errors, while image- and representation-level consistency-based methods remain underexplored in medical imaging. We propose an augmentation-sensitivity risk scoring (ASRS) framework to identify error-prone CXR cases. ASRS applies clinically plausible rotations ($\pm 15^\circ$/$\pm 30^\circ$) and measures embedding shifts with the RAD-DINO encoder. Sensitivity scores stratify samples into stability quartiles, where highly sensitive cases show substantially lower recall ($-0.2$ to $-0.3$) despite high AUROC and confidence. ASRS provides a label-free means for selective prediction and clinician review, improving fairness and safety in medical AI.

NoxTrader: LSTM-Based Stock Return Momentum Prediction

We introduce NoxTrader, which is designed for portfolio construction and trading execution, aims at generating profitable outcomes. The primary focus of NoxTrader is on stock market trading with an emphasis on cultivating moderate to long-term profits. The underlying learning process of NoxTrader hinges on the assimilation of insights gleaned from historical trading data, primarily hinging on time-series analysis due to the inherent nature of the employed dataset. We delineate the sequential progression encompassing data acquisition, feature engineering, predictive modeling, parameter configuration, establishment of a rigorous backtesting framework, and ultimately position NoxTrader as a testament to the prospective viability of algorithmic trading models within real-world trading scenarios.