1 citations · 1 across the 5 of their papers we have counts for
5 papers
OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport
Qin Ren, Yifan Wang, Ruogu Fang +2
Survival prediction using whole slide images (WSIs) can be formulated as a multiple instance learning (MIL) problem. However, existing MIL methods often fail to explicitly capture…
Morphological Profiling for Drug Discovery in the Era of Deep Learning
Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra +9
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological…
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Out-of-distribution (OOD) generalization poses a serious challenge for modern deep learning (DL). OOD data consists of test data that is significantly different from the model's tr…
LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images
Nooshin Yousefzadeh, Charlie Tran, Adolfo Ramirez-Zamora +3
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential interventi…
DOMINO: Domain-aware Loss for Deep Learning Calibration
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari +5
Deep learning has achieved the state-of-the-art performance across medical imaging tasks; however, model calibration is often not considered. Uncalibrated models are potentially da…