6 papers
Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices
Alexis Burgon, Berkman Sahiner, Nicholas A Petrick +2
This work addresses challenges in evaluating adaptive artificial intelligence (AI) models for medical devices, where iterative updates to both models and evaluation datasets compli…
Statistical modeling of breast cancer radiomic features and hazard using image registration-aided longitudinal CT data
Subrata Mukherjee, Qian Cao, Thibaud Coroller +3
Patients with metastatic breast cancer (mBC) undergo repeated computed tomography (CT) imaging during treatment to monitor disease progression. Accurate longitudinal tracking of in…
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…