130 citations
- M. Linguraru2 profiles12 · h 38
- Abhijeet Parida7 · h 8
- Zhifan Jiang7 · h 13
- S. Anwar2 profiles6 · h 8
- Austin Tapp4 · h 5
- Daniel Capellán-Martín4 · h 7
- María J. Ledesma-Carbayo4 · h 5
- Syed Muhammed Anwar4 · h 5
- A. Kazerooni3 · h 19
- Benedikt Wiestler2 profiles3 · h 31
- Bjoern H Menze3 · h 71
- C. Davatzikos2 profiles3 · h 121
- George Washington UniversityUS16 papers
- Massachusetts General HospitalUS5 papers
- Universidad Politécnica de MadridES5 papers
- Children's Hospital of PhiladelphiaUS4 papers
- Georgetown UniversityUS4 papers
- Harvard UniversityUS4 papers
- University of PennsylvaniaUS4 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS3 papers
- Cornell UniversityUS3 papers
- Indiana University – Purdue University IndianapolisUS3 papers
- Indiana University School of Medicine3 papers
- Mayo Clinic in ArizonaUS3 papers
27 papers
Foundation Models in Biomedical Imaging: Turning Hype into Reality
Amgad Muneer, Kai Zhang, Ibraheem Hamdi +6
Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrat…
Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques
Abhijeet Parida, Daniel Capellán-Martín, Zhifan Jiang +6
Gliomas are the most common malignant brain tumors in adults and are among the most lethal. Despite aggressive treatment, the median survival rate is less than 15 months. Accurate…
Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
Daniel Capellán-Martín, Abhijeet Parida, Zhifan Jiang +6
Robust and generalizable segmentation of brain tumors on multi-parametric magnetic resonance imaging (MRI) remains difficult because tumor types differ widely. The BraTS 2025 Light…
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
Daniel Capellán-Martín, Zhifan Jiang, Abhijeet Parida +8
Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This an…
mhGPT: A Lightweight Generative Pre-Trained Transformer for Mental Health Text Analysis
Dae-young Kim, Rebecca Hwa, Muhammad Mahbubur Rahman
This paper introduces mhGPT, a lightweight generative pre-trained transformer trained on mental health-related social media and PubMed articles. Fine-tuned for specific mental heal…
Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
Abhijeet Parida, Antonia Alomar, Zhifan Jiang +7
Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-spe…