172 citations · 559 across the 44 of their papers we have counts for
17 papers · 1 filter
PhyDiCT: Plug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors
Weicheng Dai, Shantanu Ghosh, Kayhan Batmanghelich
Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT,…
MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts
Weicheng Dai, Chenyu Wang, Binxu Li +4
Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for inverse problems. For these…
Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance
Chenyu Wang, Weicheng Dai, Han Liu +2
Vision--language models (VLMs) for radiology report generation (RRG) can produce long-form chest CT reports from volumetric scans and show strong potential to improve radiology wor…
VLM-UQBench: A Benchmark for Modality-Specific and Cross-Modality Uncertainties in Vision Language Models
Chenyu Wang, Tianle Chen, H. M. Sabbir Ahmad +2
Uncertainty quantification (UQ) is vital for ensuring that vision-language models (VLMs) behave safely and reliably. A central challenge is to localize uncertainty to its source, d…
Enhancing Biomedical Multi-modal Representation Learning with Multi-scale Pre-training and Perturbed Report Discrimination
Xinliu Zhong, Kayhan Batmanghelich, Li Sun
Vision-language models pre-trained on large scale of unlabeled biomedical images and associated reports learn generalizable semantic representations. These multi-modal representati…
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Ke Yu, Stephen Albro, Giulia DeSalvo +5
Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure. Active lear…
Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance
Shantanu Ghosh, Kayhan Batmanghelich
Discovering a high-performing sparse network within a massive neural network is advantageous for deploying them on devices with limited storage, such as mobile phones. Additionally…
Distilling BlackBox to Interpretable models for Efficient Transfer Learning
Shantanu Ghosh, Ke Yu, Kayhan Batmanghelich
Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Networ…
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images
Ke Yu, Li Sun, Junxiang Chen +3
Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature…
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation
Yanwu Xu, Shaoan Xie, Maxwell Reynolds +3
An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generaliz…
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays
Ke Yu, Shantanu Ghosh, Zhexiong Liu +2
Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging weak supervision from readily available data such as rad…
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
Yanwu Xu, Shaoan Xie, Wenhao Wu +3
Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution.…
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
Yanwu Xu, Mingming Gong, Shaoan Xie +1
Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe ann…
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Yipeng Mou, Mingming Gong, Huan Fu +3
Majority of state-of-the-art monocular depth estimation methods are supervised learning approaches. The success of such approaches heavily depends on the high-quality depth labels…
Robust Angular Local Descriptor Learning
Yanwu Xu, Mingming Gong, Tongliang Liu +2
In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-…
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Huan Fu, Mingming Gong, Chaohui Wang +3
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is…
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +2
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring…