29 citations · 80 across the 24 of their papers we have counts for
25 papers
FloVMos: Optical Flow-based Medical Video Mosaicking
Jinyang Liu, Sandesh Ghimire, Chaman Singh +5
Biomedical imaging modalities often require a trade-off among resolution, field of view (FOV), and acquisition speed. Video mosaicking offers a strategy to overcome this limitation…
UniVerse: A Unified Modulation Framework for Segmentation-Free,Disentangled Multi-Concept Personalization
Quynh Phung, Sandesh Ghimire, Minsi Hu +2
Personalized visual understanding has advanced significantly, yet existing approaches struggle to localize and extract specific concepts when input images contain multiple objects.…
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Jinyang Liu, Wondmgezahu Teshome, Sandesh Ghimire +2
Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video seq…
Inferring Relational Potentials in Interacting Systems
Armand Comas-Massagué, Yilun Du, Christian Fernandez +4
Systems consisting of interacting agents are prevalent in the world, ranging from dynamical systems in physics to complex biological networks. To build systems which can interact r…
Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
Nilesh Kumar, Prashnna K. Gyawali, Sandesh Ghimire +1
Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of i…
Geometry of Score Based Generative Models
Sandesh Ghimire, Jinyang Liu, Armand Comas +4
In this work, we look at Score-based generative models (also called diffusion generative models) from a geometric perspective. From a new view point, we prove that both the forward…
Divide and Compose with Score Based Generative Models
Sandesh Ghimire, Armand Comas, Davin Hill +3
While score based generative models, or diffusion models, have found success in image synthesis, they are often coupled with text data or image label to be able to manipulate and c…
Boundary-Aware Uncertainty for Feature Attribution Explainers
Davin Hill, Aria Masoomi, Max Torop +2
Post-hoc explanation methods have become a critical tool for understanding black-box classifiers in high-stakes applications. However, high-performing classifiers are often highly…
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
Max Torop, Sandesh Ghimire, Wenqian Liu +5
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OO…
Self-Supervised Decomposition, Disentanglement and Prediction of Video Sequences while Interpreting Dynamics: A Koopman Perspective
Armand Comas, Sandesh Ghimire, Haolin Li +2
Human interpretation of the world encompasses the use of symbols to categorize sensory inputs and compose them in a hierarchical manner. One of the long-term objectives of Computer…
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space
Sandesh Ghimire, Aria Masoomi, Jennifer Dy
Estimating Kullback Leibler (KL) divergence from samples of two distributions is essential in many machine learning problems. Variational methods using neural network discriminator…
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
Prashnna Kumar Gyawali, Sandesh Ghimire, Linwei Wang
The success of deep learning relies on the availability of large-scale annotated data sets, the acquisition of which can be costly, requiring expert domain knowledge. Semi-supervis…
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu +2
Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level rad…
Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
Xiajun Jiang, Sandesh Ghimire, Jwala Dhamala +3
Deep neural networks have shown great potential in image reconstruction problems in Euclidean space. However, many reconstruction problems involve imaging physics that are dependen…
Semi-supervised Medical Image Classification with Global Latent Mixing
Prashnna Kumar Gyawali, Sandesh Ghimire, Pradeep Bajracharya +2
Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates…
High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
Jwala Dhamala, Sandesh Ghimire, John L. Sapp +2
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatia…
Analysis of Discriminator in RKHS Function Space for Kullback-Leibler Divergence Estimation
Sandesh Ghimire, Prashnna K Gyawali, Linwei Wang
Several scalable sample-based methods to compute the Kullback Leibler (KL) divergence between two distributions have been proposed and applied in large-scale machine learning model…
Improving Disentangled Representation Learning with the Beta Bernoulli Process
Prashnna Kumar Gyawali, Zhiyuan Li, Cameron Knight +4
To improve the ability of VAE to disentangle in the latent space, existing works mostly focus on enforcing independence among the learned latent factors. However, the ability of th…
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space
Prashnna Kumar Gyawali, Zhiyuan Li, Sandesh Ghimire +1
The success of deep learning in medical imaging is mostly achieved at the cost of a large labeled data set. Semi-supervised learning (SSL) provides a promising solution by leveragi…
Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
Jwala Dhamala, Sandesh Ghimire, John L. Sapp +2
Personalization of cardiac models involves the optimization of organ tissue properties that vary spatially over the non-Euclidean geometry model of the heart. To represent the high…
A Variational Approach to Sparse Model Error Estimation in Cardiac Electrophysiological Imaging
Sandesh Ghimire, John L Sapp, Milan Horacek +1
Noninvasive reconstruction of cardiac electrical activity from surface electrocardiograms (ECG) involves solving an ill-posed inverse problem. Cardiac electrophysiological (EP) mod…
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali +3
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is…
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Sandesh Ghimire, Prashnna Kumar Gyawali, Jwala Dhamala +3
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems. However, it is not well understood what properties of representation and learn…
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors
Prashnna K Gyawali, Cameron Knight, Sandesh Ghimire +3
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenge…