18 citations · 100 across the 31 of their papers we have counts for
33 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…
Atlas of MICCAI: 28 Years of Representation, Collaboration, and the Road to Inclusion
Amelia Jiménez-Sánchez, Laura Arbelaez Ossa, Joan Perramon-Llussà +6
Scientific knowledge in medical image analysis is shaped by venues such as the Medical Image Computing and Computer-Assisted Intervention (MICCAI) conference, which influence resea…
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…
MRI-to-CT Synthesis With Cranial Suture Segmentations Using A Variational Autoencoder Framework
Krithika Iyer, Austin Tapp, Athelia Paulli +4
Quantifying normative pediatric cranial development and suture ossification is crucial for diagnosing and treating growth-related cephalic disorders. Computed tomography (CT) is wi…
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…
Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
Daria Laslo, Efthymios Georgiou, Marius George Linguraru +4
Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that co…
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki +34
The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…
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.…
Graph-Based Deep Learning on Stereo EEG for Predicting Seizure Freedom in Epilepsy Patients
Artur Agaronyan, Syeda Abeera Amir, Nunthasiri Wittayanakorn +5
Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, especially with diverse patient popu…
Geometric Deep Learning for Automated Landmarking of Maxillary Arches on 3D Oral Scans from Newborns with Cleft Lip and Palate
Artur Agaronyan, HyeRan Choo, Marius Linguraru +1
Rapid advances in 3D model scanning have enabled the mass digitization of dental clay models. However, most clinicians and researchers continue to use manual morphometric analysis…
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…
A New Logic For Pediatric Brain Tumor Segmentation
Max Bengtsson, Elif Keles, Gorkem Durak +5
In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our m…
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…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…