Abhijeet Parida
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View article: Post-Processing Methods for Improving Accuracy in MRI Inpainting
Post-Processing Methods for Improving Accuracy in MRI Inpainting Open
Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologies. However, most automated MRI analysis tools, such as segmentation and registration pipelines, …
View article: Analyzing pediatric forearm X-rays for fracture analysis using machine learning
Analyzing pediatric forearm X-rays for fracture analysis using machine learning Open
View article: MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models
MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models Open
View article: Predicting Anatomical Brain Tumor Growth by Guided Denoising Diffusion Models
Predicting Anatomical Brain Tumor Growth by Guided Denoising Diffusion Models Open
View article: Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation Open
Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The Inter…
View article: Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data Open
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. This paper introduces a novel approach to glioma segmentatio…
View article: Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging Open
Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-m…
View article: Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration Open
Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning. For histopathology, stain normalization …
View article: MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models
MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models Open
Federated learning (FL) allows participants to collaboratively train machine learning models while keeping their data local, making it ideal for collaborations among healthcare institutions on sensitive data. However, in this paper, we pro…
View article: D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions Open
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 assistant for biomedicine, can perform multi-modal biomedical im…
View article: NFS-23. ASSOCIATION OF LOCALIZED MAGNETIC RESONANCE IMAGING FEATURES IN ANTERIOR VISUAL PATHWAY WITH VISUAL ACUITY LOSS AMONG CHILDREN WITH NF1-OPG
NFS-23. ASSOCIATION OF LOCALIZED MAGNETIC RESONANCE IMAGING FEATURES IN ANTERIOR VISUAL PATHWAY WITH VISUAL ACUITY LOSS AMONG CHILDREN WITH NF1-OPG Open
BACKGROUND Half of children with optic pathway gliomas associated with neurofibromatosis type 1 (NF1-OPG) are at risk for vision acuity (VA) loss. NF1-OPGs manifest along the anterior visual pathway (AVP), where the overall shape and volum…
View article: IMG-29. IMPLICATIONS OF IMAGE HARMONIZATION FOR BRAIN TUMOR CLINICAL TRIALS
IMG-29. IMPLICATIONS OF IMAGE HARMONIZATION FOR BRAIN TUMOR CLINICAL TRIALS Open
BACKGROUND Clinical trials for rare pediatric conditions like optic pathway gliomas associated with NF1 (NF1-OPGs) require numerous sites that utilize different MRI scanners and protocols. These MRIs must be harmonized for reliable compari…
View article: IMG-28. AUTOMATIC BRAIN TUMOR VOLUMETRIC ANALYSIS IN MAGNETIC RESONANCE IMAGING GENERALIZABLE TO PEDIATRIC NEURO-ONCOLOGY
IMG-28. AUTOMATIC BRAIN TUMOR VOLUMETRIC ANALYSIS IN MAGNETIC RESONANCE IMAGING GENERALIZABLE TO PEDIATRIC NEURO-ONCOLOGY Open
BACKGROUND The prognosis of brain tumors is variable in clinical practice if it only relies on human interpretation of magnetic resonance imaging (MRI). The automatic segmentation of brain tumors in MRI enables quantitative analysis in sup…
View article: NFS-24. TUMOR VOLUME IN NEWLY DIAGNOSED OPTIC PATHWAY GLIOMAS ASSOCIATED WITH NF1 (NF1-OPG): PRELIMINARY RESULTS FROM THE INTERNATIONAL MULTICENTER NF1-OPG NATURAL HISTORY STUDY
NFS-24. TUMOR VOLUME IN NEWLY DIAGNOSED OPTIC PATHWAY GLIOMAS ASSOCIATED WITH NF1 (NF1-OPG): PRELIMINARY RESULTS FROM THE INTERNATIONAL MULTICENTER NF1-OPG NATURAL HISTORY STUDY Open
BACKGROUND NF1-OPGs are amorphous tumors involving either single or multiple locations (optic nerve, chiasm, tract) along the anterior visual pathway (AVP). In this prospective study, we investigated how volumetric MRI measures of the AVP …
View article: Quantitative Metrics for Benchmarking Medical Image Harmonization
Quantitative Metrics for Benchmarking Medical Image Harmonization Open
Image harmonization is an important preprocessing strategy to address domain\nshifts arising from data acquired using different machines and scanning\nprotocols in medical imaging. However, benchmarking the effectiveness of\nharmonization …
View article: Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays Using Self-Supervised Learning
Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays Using Self-Supervised Learning Open
Tuberculosis (TB) remains a significant global health challenge, with\npediatric cases posing a major concern. The World Health Organization (WHO)\nadvocates for chest X-rays (CXRs) for TB screening. However, visual\ninterpretation by radi…
View article: Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI Open
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 that may use different devices and protocols. Deep learning-dr…
View article: Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging
Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging Open
Children with optic pathway gliomas (OPGs), a low-grade brain tumor associated with neurofibromatosis type 1 (NF1-OPG), are at risk for permanent vision loss. While OPG size has been associated with vision loss, it is unclear how changes i…
View article: SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model Open
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 segmentation. There is a large body of work where machine …
View article: GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images Open
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 generated images that can be useful in training other models th…
View article: Train, Learn, Expand, Repeat
Train, Learn, Expand, Repeat Open
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, labeling poses a major challenge: medical professionals who can ex…
View article: Learn to Segment Organs with a Few Bounding Boxes
Learn to Segment Organs with a Few Bounding Boxes Open
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 can perform this task well by leveraging the information fr…
View article: Studies on Biometric Parameters of Cashew in Bhubaneswer Condition
Studies on Biometric Parameters of Cashew in Bhubaneswer Condition Open
An investigation was laid out in cashew research station under AICRP on cashew Ranasinghapur, Bhubaneswar, Khurda during the year 2015-2017, to study the variability in biometric parameters of thirty land races of cashew such as plant heig…