2 citations · 2 across the 4 of their papers we have counts for
4 papers
Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT
Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples +2
We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) for liver tumor segmentation in abdominal CT of colorectal liver metastases. We compared Low…
Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography
Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples +6
Accurate segmentation of colorectal liver metastases (CRLM) in contrast-enhanced computed tomography (CT) is important for response assessment, surgical planning, and follow-up. We…
A Novel Patch-Based TDA Approach for Computed Tomography Imaging
Dashti A. Ali, Aras T. Asaad, Jacob J. Peoples +11
The development of machine learning models based on computed tomography (CT) imaging has been a major focus due to the promise that imaging holds for diagnosis, staging, and progno…
Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases
Jacob J. Peoples, Mohammad Hamghalam, Imani James +7
Establishing the reproducibility of radiomic signatures is a critical step in the path to clinical adoption of quantitative imaging biomarkers; however, radiomic signatures must al…