14 citations · 21 across the 6 of their papers we have counts for
7 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…
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…
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…
Parameter-Efficient Methods for Metastases Detection from Clinical Notes
Maede Ashofteh Barabadi, Xiaodan Zhu, Wai Yip Chan +2
Understanding the progression of cancer is crucial for defining treatments for patients. The objective of this study is to automate the detection of metastatic liver disease from f…
Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation
Ramtin Mojtahedi, Mohammad Hamghalam, Richard K. G. Do +1
Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential role in the early diagnosis and treatment of liver cancer. Deep learning models backboned by fully con…
Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases
Mohammad Hamghalam, Richard K. G. Do, Amber L. Simpson
Small liver lesions common to colorectal liver metastases (CRLMs) are challenging for convolutional neural network (CNN) segmentation models, especially when we have a wide range o…