papers

Publications (9)

q-bio.QM2023

A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

Divya Ramakrishnan, Leon Jekel, Saahil Chadha +19

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effect…

eess.IV2025

On hallucinations in AI-generated content for nuclear medicine imaging (the DREAM report)

Menghua Xia, Reimund Bayerlein, Yanis Chemli +7

Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software solutions for tasks such as im…

eess.IV2019

Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation

Junlin Yang, Nicha C. Dvornek, Fan Zhang +3

A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised d…

q-bio.OT2025

Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI

Nazanin Maleki, Raisa Amiruddin, Ahmed W. Moawad +240

Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis.…

q-bio.OT2024

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

Ahmed W. Moawad, Anastasia Janas, Ujjwal Baid +229

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS…

eess.IV2019

Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making

Junlin Yang, Nicha C. Dvornek, Fan Zhang +4

This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our…