7 citations · 13 across the 9 of their papers we have counts for
9 papers
S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images
Andrea Kim, Niloufar Saharkhiz, Elena Sizikova +4
Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining…
Synthetic Data in Radiological Imaging: Current State and Future Outlook
Elena Sizikova, Andreu Badal, Jana G. Delfino +6
A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and repre…
Image registration based automated lesion correspondence pipeline for longitudinal CT data
Subrata Mukherjee, Thibaud Coroller, Craig Wang +6
Patients diagnosed with metastatic breast cancer (mBC) typically undergo several radiographic assessments during their treatment. mBC often involves multiple metastatic lesions in…
TorchSurv: A Lightweight Package for Deep Survival Analysis
Mélodie Monod, Peter Krusche, Qian Cao +4
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment. Unlike existing libraries that impose specific param…
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson +4
Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical sett…
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma +4
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 ca…