390 citations · 469 across the 11 of their papers we have counts for
11 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…
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…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation
Mohammad Hamghalam, Alejandro F. Frangi, Baiying Lei +1
In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e.g. imaging…
Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans
Mohammad Hamghalam, Baiying Lei, Tianfu Wang
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for…
High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation
Mohammad Hamghalam, Baiying Lei, Tianfu Wang
Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent…
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans
Mohammad Hamghalam, Baiying Lei, Tianfu Wang
The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions. The overlapping area among the intensity distribution of heal…