Healthcare

xMIL-LRP: Enhanced explainable AI for multiple instance learning in histopathology, boosting model transparency and enabling new knowledge discovery.

Researchers developed a causally-motivated approach for ranking agents based on their gaming propensity, addressing the challenge of identifying ‘worst offenders’ in strategic classification settings.

Boosting clinical trial efficiency, this research introduces a covariate-adjusted response-adaptive randomization (CARA) design that effectively leverages surrogate outcomes to handle delayed primary …

Fast disease progression inference is achieved via optimal transport, enabling high-dimensional, interpretable models and offering broad clinical applications.

AI can unlock the potential of global human expertise for solving complex problems by combining diverse expert solutions using an evolutionary framework, resulting in better and more effective strateg…

UniMTS, a novel unified pre-training model, achieves state-of-the-art performance in motion time series classification by generalizing across diverse device factors and activities.

MPI: A novel framework harnesses multi-modal biological data via view decoupling for superior phenotype imputation.

UniGuide: A unified framework for geometry-conditioned molecular generation using unconditional diffusion models, enabling flexible conditioning without extra training or networks.

TurboHopp: 30x faster 3D scaffold hopping with consistency models, boosting drug discovery!

Simulation-free Neural SDE training via Trajectory Flow Matching unlocks scalability and stability for modeling complex real-world time series, particularly in clinical settings.

Med-MICN: a novel end-to-end framework for medical image classification, achieving superior accuracy and multi-dimensional interpretability by aligning neural symbolic reasoning, concept semantics, an…

This study introduces novel, scalable AI-based planning strategies for controlling misinformation spread in dynamic opinion networks, significantly improving infection rate control.

Boost survival prediction accuracy with CSD-iPOT: a novel post-processing method achieving superior marginal & conditional calibration without sacrificing discrimination.

ConSurv: a novel contrastive learning approach for survival analysis enhances discrimination without sacrificing calibration by employing weighted sampling and aligning well with the assumption that p…

Task-oriented time series imputation is revolutionized! This research introduces a novel approach that efficiently assesses imputation strategies based on downstream task performance without costly mo…

SMART: a novel self-supervised model tackles missing EHR data, improving patient health status prediction via missing-aware attention and robust pre-training.

SmMIL enhances medical image classification by introducing a novel smooth operator to improve instance-level localization in multiple instance learning, achieving state-of-the-art results.

Sequence-augmented SE(3)-Flow model, FOLDFLOW-2, excels at generating diverse, designable protein structures, surpassing existing methods in unconditional and conditional design tasks.

Self-Calibrating Conformal Prediction (SC-CP) marries model calibration and conformal prediction for more efficient and interpretable prediction intervals with prediction-conditional validity.

RoME, a robust contextual bandit algorithm, leverages mixed-effects modeling and debiased machine learning to optimize personalized mobile health interventions, achieving superior performance in simul…