99 citations · 157 across the 46 of their papers we have counts for
50 papers
NNV3: Expanding Neural Network Verification to New Architectures and Domains
Anne M. Tumlin, Samuel Sasaki, Ben Wooding +8
We present NNV3, the latest version of the Neural Network Verification (NNV) tool, a MATLAB framework for formal verification of deep learning models and learning-enabled cyber-phy…
LISynSeg: Data-Centric Label-to-Image Synthesis for Cross-Modality Whole-Heart Segmentation
Jiacheng Wang, Ivana Isgum, Ipek Oguz
Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous cardiac annotations. Existing WH…
Towards Verified and Targeted Explanations through Formal Methods
Hanchen David Wang, Diego Manzanas Lopez, Preston K. Robinette +3
As deep neural networks are deployed in safety-critical domains such as autonomous driving and medical diagnosis, stakeholders need explanations that are interpretable but also tru…
VAMOS-OCTA: Vessel-Aware Multi-Axis Orthogonal Supervision for Inpainting Motion-Corrupted OCT Angiography Volumes
Nick DiSanto, Ehsan Khodapanah Aghdam, Han Liu +4
Handheld Optical Coherence Tomography Angiography (OCTA) enables noninvasive retinal imaging in uncooperative or pediatric subjects, but is highly susceptible to motion artifacts t…
IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation
Han Liu, Yubo Fan, Hao Li +5
Segmentation of vestibular schwannoma and cochlea from T2 MRI is clinically important yet annotation-intensive. Domain adaptation (DA) has been widely adopted to bridge the gap bet…
EndoStreamDepth: Temporally Consistent Monocular Depth Estimation for Endoscopic Video Streams
Hao Li, Daiwei Lu, Jiacheng Wang +2
This work presents EndoStreamDepth, a monocular depth estimation framework for endoscopic video streams. It provides accurate depth maps with sharp anatomical boundaries for each f…
Endo-SemiS: Towards Robust Semi-Supervised Image Segmentation for Endoscopic Video
Hao Li, Daiwei Lu, Xing Yao +2
In this paper, we present Endo-SemiS, a semi-supervised segmentation framework for providing reliable segmentation of endoscopic video frames with limited annotation. EndoSemiS use…
ProbeMDE: Uncertainty-Guided Active Proprioception for Monocular Depth Estimation in Surgical Robotics
Britton Jordan, Jordan Thompson, Jesse F. d'Almeida +8
Monocular depth estimation (MDE) provides a useful tool for robotic perception, but its predictions are often uncertain and inaccurate in challenging environments such as surgical…
DEMIST: Decoupled Multi-stream latent diffusion for Quantitative Myelin Map Synthesis
Jiacheng Wang, Hao Li, Xing Yao +9
Quantitative magnetization transfer (qMT) imaging provides myelin-sensitive biomarkers, such as the pool size ratio (PSR), which is valuable for multiple sclerosis (MS) assessment.…
A Supervised Autonomous Resection and Retraction Framework for Transurethral Enucleation of the Prostatic Median Lobe
Mariana Smith, Tanner Watts, Susheela Sharma Stern +15
Concentric tube robots (CTRs) offer dexterous motion at millimeter scales, enabling minimally invasive procedures through natural orifices. This work presents a coordinated model-b…
Monocular absolute depth estimation from endoscopy via domain-invariant feature learning and latent consistency
Hao Li, Daiwei Lu, Jesse d'Almeida +8
Monocular depth estimation (MDE) is a critical task to guide autonomous medical robots. However, obtaining absolute (metric) depth from an endoscopy camera in surgical scenes is di…
PERSEUS: Perception with Semantic Endoscopic Understanding and SLAM
Ayberk Acar, Fangjie Li, Susheela Sharma Stern +9
Purpose: Natural orifice surgeries minimize the need for incisions and reduce the recovery time compared to open surgery; however, they require a higher level of expertise due to v…
Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network
Navid Hashemi, Samuel Sasaki, Diego Manzanas Lopez +4
Semantic segmentation networks (SSNs) are central to safety-critical applications such as medical imaging and autonomous driving, where robustness under uncertainty is essential. H…
crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory +38
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assist…
From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction
Ayberk Acar, Mariana Smith, Lidia Al-Zogbi +14
Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however t…
SynStitch: a Self-Supervised Learning Network for Ultrasound Image Stitching Using Synthetic Training Pairs and Indirect Supervision
Xing Yao, Runxuan Yu, Dewei Hu +10
Ultrasound (US) image stitching can expand the field-of-view (FOV) by combining multiple US images from varied probe positions. However, registering US images with only partially o…
AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation
Dewei Hu, Hao Li, Han Liu +4
Deep learning has shown remarkable performance in medical image segmentation. However, despite its promise, deep learning has many challenges in practice due to its inability to ef…
PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound
Hao Li, Baris Oguz, Gabriel Arenas +6
Placenta volume measured from 3D ultrasound (3DUS) images is an important tool for tracking the growth trajectory and is associated with pregnancy outcomes. Manual segmentation is…
Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets
Hao Li, Han Liu, Heinrich von Busch +16
Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised lea…
Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
Jiacheng Wang, Hao Li, Dewei Hu +4
We propose a novel framework for retinal feature point alignment, designed for learning cross-modality features to enhance matching and registration across multi-modality retinal i…
Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images
Hao Li, Baris Oguz, Gabriel Arenas +6
Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is…
Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
Han Liu, Hao Li, Jiacheng Wang +3
Fluorescence labeling is the standard approach to reveal cellular structures and other subcellular constituents for microscopy images. However, this invasive procedure may perturb…
PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Hao Li, Han Liu, Dewei Hu +2
In this paper, we present PRISM, a Promptable and Robust Interactive Segmentation Model, aiming for precise segmentation of 3D medical images. PRISM accepts various visual inputs,…
Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
Han Liu, Yubo Fan, Zhoubing Xu +2
Unsupervised cross-modality domain adaptation is a challenging task in medical image analysis, and it becomes more challenging when source and target domain data are collected from…