51 citations · 54 across the 20 of their papers we have counts for
21 papers
Beyond MSE: Rician Likelihood Denoising for Self-Supervised Cardiac and MRI
Nicholas A. Jacobs, Jason Mendes, Ravi Ranjan +2
Magnetic resonance imaging involves an inherent trade-off among spatial resolution, acquisition time, and noise. This trade-off contributes to long scan times and high cost. Deep l…
SIMPLER: H&E-Informed Representation Learning for Structured Illumination Microscopy
Abu Zahid Bin Aziz, Syed Fahim Ahmed, Gnanesh Rasineni +8
Structured Illumination Microscopy (SIM) enables rapid, high-contrast optical sectioning of fresh tissue without staining or physical sectioning, making it promising for intraopera…
PC-MIL: Decoupling Feature Resolution from Supervision Scale in Whole-Slide Learning
Syed Fahim Ahmed, Gnanesh Rasineni, Florian Koehler +7
Whole-slide image (WSI) classification in computational pathology is commonly formulated as slide-level Multiple Instance Learning (MIL) with a single global bag representation. Ho…
MedConcept: Unsupervised Concept Discovery for Interpretability in Medical VLMs
Md Rakibul Haque, KM Arefeen Sultan, Tushar Kataria +1
While medical Vision-Language models (VLMs) achieve strong performance on tasks such as tumor or organ segmentation and diagnosis prediction, their opaque latent representations li…
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance
Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Elhabian
Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed l…
AC-MIL: Weakly Supervised Atrial LGE-MRI Quality Assessment via Adversarial Concept Disentanglement
K M Arefeen Sultan, Kaysen Hansen, Benjamin Orkild +7
High-quality Late Gadolinium Enhancement (LGE) MRI can be helpful for atrial fibrillation management, yet scan quality is frequently compromised by patient motion, irregular breath…
Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI
Md Hasibul Husain Hisham, Shireen Elhabian, Ganesh Adluru +5
Accelerated 3D late gadolinium enhancement (LGE) MRI requires robust reconstruction methods to recover thin atrial structures from undersampled k-space data. While unrolled model-b…
Adaptive Particle-Based Shape Modeling for Anatomical Surface Correspondence
Hong Xu, Shireen Y. Elhabian
Particle-based shape modeling (PSM) is a family of approaches that automatically quantifies shape variability across anatomical cohorts by positioning particles (pseudo landmarks)…
IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions
Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian
Hematoxylin and eosin (H&E)-stained slides are central to cancer diagnosis and monitoring, visualizing tissue architecture and cellular morphology. However, H&E lacks the molecular…
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Mokshagna Sai Teja Karanam, Krithika Iyer, Sarang Joshi +1
Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness regularizers for image registration…
LEDA: Log-Euclidean Diffeomorphism Autoencoder for Efficient Statistical Analysis of Diffeomorphisms
Krithika Iyer, Shireen Elhabian, Sarang Joshi
Image registration is a core task in computational anatomy that establishes correspondences between images. Invertible deformable registration, which computes a deformation field a…
HAMIL-QA: Hierarchical Approach to Multiple Instance Learning for Atrial LGE MRI Quality Assessment
K M Arefeen Sultan, Md Hasibul Husain Hisham, Benjamin Orkild +7
The accurate evaluation of left atrial fibrosis via high-quality 3D Late Gadolinium Enhancement (LGE) MRI is crucial for atrial fibrillation management but is hindered by factors l…
Learning Deep Features for Shape Correspondence with Domain Invariance
Praful Agrawal, Ross T. Whitaker, Shireen Y. Elhabian
Correspondence-based shape models are key to various medical imaging applications that rely on a statistical analysis of anatomies. Such shape models are expected to represent cons…
Visualization of topology optimization designs with representative subset selection
Daniel J Perry, Vahid Keshavarzzadeh, Shireen Y Elhabian +3
An important new trend in additive manufacturing is the use of optimization to automatically design industrial objects, such as beams, rudders or wings. Topology optimization, as i…
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Anupama Goparaju, Alexandre Bone, Nan Hu +8
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technolog…
Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
Hong Xu, David E. Timm, Shireen Y. Elhabian
Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (les…
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration
Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan +1
Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such tra…
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
Tim Sodergren, Riddhish Bhalodia, Ross Whitaker +3
Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Noneth…
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Anupama Goparaju, Ibolya Csecs, Alan Morris +4
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical…
DeepSSM: A Deep Learning Framework for Statistical Shape Modeling from Raw Images
Riddhish Bhalodia, Shireen Y. Elhabian, Ladislav Kavan +1
Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeli…
Clustering With Pairwise Relationships: A Generative Approach
Yen-Yun Yu, Shireen Y. Elhabian, Ross T. Whitaker
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limite…