21 citations · 22 across the 10 of their papers we have counts for
11 papers
MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalities
Tarun Kumar Garg, Vaanathi Sundaresan
Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centraliz…
AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages
Maunil Shah, Vaanathi Sundaresan
Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning. Non-contrast CT (NCCT), the first-line imaging modal…
FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging
Harsh Kumar, Tarun Kumar Garg, Vaanathi Sundaresan
Federated learning (FL) is severely hindered by statistical heterogeneity due to variations in scanners, acquisition protocols, and patient populations. Such non-IID data induces c…
Mutually Exclusive Multiclass Lesion Segmentation in Neuroimaging: Binary-Guided Weak Supervision with Inter-Class Orthogonality
Ashutosh Kumar, Vivek Dhamale, Vaanathi Sundaresan
Weakly supervised segmentation of co-occurring neuroimaging lesion subclasses remains challenging due to overlapping activations, noisy pseudo-labels, and the absence of explicit i…
MARVEL: Margin-Aware Robust von Mises-Fischer Expert Learning for Long-Tailed Out-of-Distribution Detection
A. S. Anudeep, Vaanathi Sundaresan
For clinical deployment, it is essential that automated diagnostic systems remain reliable when confronted with previously unseen cases, yet deep models routinely misclassify out-o…
Traumatic Brain Injury Segmentation using an Ensemble of Encoder-decoder Models
Ghanshyam Dhamat, Vaanathi Sundaresan
The identification and segmentation of moderate-severe traumatic brain injury (TBI) lesions pose a significant challenge in neuroimaging. This difficulty arises from the extreme he…
Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain MR images
Vaanathi Sundaresan, Nicola K Dinsdale
Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many fac…
Class Activation Map-based Weakly supervised Hemorrhage Segmentation using Resnet-LSTM in Non-Contrast Computed Tomography images
Shreyas H Ramananda, Vaanathi Sundaresan
In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) for severity assessment. Accurate automated segmentation of ICH lesions is…
Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data
Vaanathi Sundaresan, Julia F. Lehman, Sean Fitzgibbon +3
Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detectio…
Challenges for machine learning in clinical translation of big data imaging studies
Nicola K Dinsdale, Emma Bluemke, Vaanathi Sundaresan +3
The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the su…
Brain tumour segmentation using a triplanar ensemble of U-Nets
Vaanathi Sundaresan, Ludovica Griffanti, Mark Jenkinson
Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challen…