2 citations · 2 across the 4 of their papers we have counts for
3 papers · 1 filter
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…