18 citations · 100 across the 31 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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…
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
Maria Correia de Verdier, Rachit Saluja, Louis Gagnon +82
Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the gene…
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Dominic LaBella, Valeriia Abramova, Mehdi Astaraki +102
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional da…
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +68
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less…
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)…