5 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…
Agentic Autoresearch for CT Reconstruction
Andreas Maier, Lucas Kachelriess, Siming Bayer +4
Comparing CT reconstruction methods fairly is labor-intensive and largely manual, and many benchmarks use idealized data. We ask whether a large language model (LLM) agent can do t…
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…
Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging
Dashti A. Ali, Richard K. G. Do, William R. Jarnagin +2
In medical image analysis, feature engineering plays an important role in the design and performance of machine learning models. Persistent homology (PH), from the field of topolog…