12 citations · 44 across the 22 of their papers we have counts for
23 papers
Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation
Abhijeet Parida, Zhifan Jiang, Pooneh Roshanitabrizi +6
Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute b…
VolTA-3D: Self-Supervised Learning for Brain MRI using 3D Volumetric Token Alignment
Amy Makawana, Abhijeet Parida, Marius George Linguraru +2
Self-supervised learning (SSL) has advanced medical image analysis be enabling learning form large unlabelled data. However, in brain magnetic resonance imaging (MRI), most 3D mode…
LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis
Zhifan Jiang, Dong Yang, Vishwesh Nath +7
Large vision-language models (VLMs) have evolved from general-purpose applications to specialized use cases such as in the clinical domain, demonstrating potential for decision sup…
Standardized Methods and Recommendations for Green Federated Learning
Austin Tapp, Holger R. Roth, Ziyue Xu +3
Federated learning (FL) enables collaborative model training over privacy-sensitive, distributed data, but its environmental impact is difficult to compare across studies due to in…
FeTTL: Federated Template and Task Learning for Multi-Institutional Medical Imaging
Abhijeet Parida, Antonia Alomar, Zhifan Jiang +7
Federated learning enables collaborative model training across geographically distributed medical centers while preserving data privacy. However, domain shifts and heterogeneity in…
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…
Post-Processing Methods for Improving Accuracy in MRI Inpainting
Nishad Kulkarni, Krithika Iyer, Austin Tapp +6
Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologies. However, most automated MRI analys…
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data
Abhijeet Parida, Daniel Capellán-Martín, Zhifan Jiang +5
Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic challenges in low- and middle-income countries, particularly in Sub-Saharan Africa. T…
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
Zhifan Jiang, Daniel Capellán-Martín, Abhijeet Parida +5
Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly im…
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…
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions
Hareem Nisar, Syed Muhammad Anwar, Zhifan Jiang +5
Large vision language models (VLMs) have progressed incredibly from research to applicability for general-purpose use cases. LLaVA-Med, a pioneering large language and vision assis…
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images
Furqan Shaukat, Syed Muhammad Anwar, Abhijeet Parida +3
Lung cancer has been one of the major threats to human life for decades. Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characte…
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…
MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models
Shanghao Shi, Md Shahedul Haque, Abhijeet Parida +5
Federated learning (FL) allows participants to collaboratively train machine learning models while keeping their data local, making it ideal for collaborations among healthcare ins…
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
Daniel Capellán-Martín, Abhijeet Parida, Juan J. Gómez-Valverde +5
Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs)…
Quantitative Metrics for Benchmarking Medical Image Harmonization
Abhijeet Parida, Zhifan Jiang, Roger J. Packer +3
Image harmonization is an important preprocessing strategy to address domain shifts arising from data acquired using different machines and scanning protocols in medical imaging. H…
DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
Abhijeet Parida, Daniel Capellan-Martin, Sara Atito +4
Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automa…
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
Abhijeet Parida, Zhifan Jiang, Syed Muhammad Anwar +6
For machine learning-based prognosis and diagnosis of rare diseases, such as pediatric brain tumors, it is necessary to gather medical imaging data from multiple clinical sites tha…
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
Syed Muhammad Anwar, Abhijeet Parida, Sara Atito +4
Chest X-rays (CXRs) are a widely used imaging modality for the diagnosis and prognosis of lung disease. The image analysis tasks vary. Examples include pathology detection and lung…
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
Aadhithya Sankar, Matthias Keicher, Rami Eisawy +4
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically gene…
Train, Learn, Expand, Repeat
Abhijeet Parida, Aadhithya Sankar, Rami Eisawy +4
High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labelin…
Learn to Segment Organs with a Few Bounding Boxes
Abhijeet Parida, Arianne Tran, Nassir Navab +1
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology. Deep neural networks…