no code implementations • 25 Apr 2024 • Subrata Mukherjee, Thibaud Coroller, Craig Wang, Ravi K. Samala, Tingting Hu, Didem Gokcay, Nicholas Petrick, Berkman Sahiner, Qian Cao
The algorithm employs a sequential two-step pipeline: (a) Firstly, an adaptive Hungarian algorithm is used to establish correspondence among lesions within a single volumetric image series which have been annotated by multiple radiologists at a specific timepoint.
1 code implementation • 16 Apr 2024 • Mélodie Monod, Peter Krusche, Qian Cao, Berkman Sahiner, Nicholas Petrick, David Ohlssen, Thibaud Coroller
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment.
no code implementations • 12 Feb 2024 • Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson, Smriti Prathapan, Paul H. Yi, Berkman Sahiner, Jana G. Delfino
Conclusion: We propose a framework for OOD detection and drift monitoring that is agnostic to data, modality, and model.
no code implementations • 20 Nov 2023 • Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
1 code implementation • NeurIPS 2023 • Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma, Miguel Lago, Berkman Sahiner, Jana G. Delfino, Aldo Badano
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available.
1 code implementation • 28 Jul 2023 • Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup.
1 code implementation • 17 Nov 2022 • Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict.
no code implementations • 21 Mar 2022 • Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann
Each modification introduces a risk of deteriorating performance and must be validated on a test dataset.
1 code implementation • 13 Oct 2021 • Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio
In the COPD study, BLR and MarBLR dynamically combined the original model with a continually-refitted gradient boosted tree to achieve aAUCs of 0. 924 (95%CI 0. 913-0. 935) and 0. 925 (95%CI 0. 914-0. 935), compared to the static model's aAUC of 0. 904 (95%CI 0. 892-0. 916).