126 citations · 734 across the 65 of their papers we have counts for
67 papers
A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video
Jan Emily Mangulabnan, Roger D. Soberanis-Mukul, Timo Teufel +9
Generating accurate 3D reconstructions from endoscopic video is a promising avenue for longitudinal radiation-free analysis of sinus anatomy and surgical outcomes. Several methods…
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning
Nathan Drenkow, Mathias Unberath
Object-centric representation learning offers the potential to overcome limitations of image-level representations by explicitly parsing image scenes into their constituent compone…
TransNuSeg: A Lightweight Multi-Task Transformer for Nuclei Segmentation
Zhenqi He, Mathias Unberath, Jing Ke +1
Nuclei appear small in size, yet, in real clinical practice, the global spatial information and correlation of the color or brightness contrast between nuclei and background, have…
Automated Artifact Detection in Ultra-widefield Fundus Photography of Patients with Sickle Cell Disease
Anqi Feng, Dimitri Johnson, Grace R. Reilly +5
Importance: Ultra-widefield fundus photography (UWF-FP) has shown utility in sickle cell retinopathy screening; however, image artifact may diminish quality and gradeability of ima…
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Zhaoshuo Li, Thomas Müller, Alex Evans +4
Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed…
Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation
Benjamin D. Killeen, Han Zhang, Jan Mangulabnan +4
Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorpora…
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models
Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick +2
To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorit…
Task-based Generation of Optimized Projection Sets using Differentiable Ranking
Linda-Sophie Schneider, Mareike Thies, Christopher Syben +3
We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis. The approach integrates two important factor…
MoViT: Memorizing Vision Transformers for Medical Image Analysis
Yiqing Shen, Pengfei Guo, Jingpu Wu +5
The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and…
TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery
Zhaoshuo Li, Hongchao Shu, Ruixing Liang +5
Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used…
Rethinking Causality-driven Robot Tool Segmentation with Temporal Constraints
Hao Ding, Jie Ying Wu, Zhaoshuo Li +1
Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots and downstream tasks. CaRTS, based on a complementary causal model, has shown promising pe…
Twin-S: A Digital Twin for Skull-base Surgery
Hongchao Shu, Ruixing Liang, Zhaoshuo Li +9
Purpose: Digital twins are virtual interactive models of the real world, exhibiting identical behavior and properties. In surgical applications, computational analysis from digital…
Context-Enhanced Stereo Transformer
Weiyu Guo, Zhaoshuo Li, Yongkui Yang +5
Stereo depth estimation is of great interest for computer vision research. However, existing methods struggles to generalize and predict reliably in hazardous regions, such as larg…
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments
Cong Gao, Benjamin D. Killeen, Yicheng Hu +4
Artificial intelligence (AI) now enables automated interpretation of medical images for clinical use. However, AI's potential use for interventional images (versus those involved i…
CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data
Hao Ding, Jintan Zhang, Peter Kazanzides +2
Vision-based segmentation of the robotic tool during robot-assisted surgery enables downstream applications, such as augmented reality feedback, while allowing for inaccuracies in…
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
Xingtong Liu, Zhaoshuo Li, Masaru Ishii +3
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry…
Mapping DNN Embedding Manifolds for Network Generalization Prediction
Molly O'Brien, Julia Bukowski, Mathias Unberath +2
Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e.g., pe…
Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review
Haomin Chen, Catalina Gomez, Chien-Ming Huang +1
Transparency in Machine Learning (ML), attempts to reveal the working mechanisms of complex models. Transparent ML promises to advance human factors engineering goals of human-cent…
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?
Nathan Drenkow, Numair Sani, Ilya Shpitser +1
Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-impactful applications, motivating the need to close the gap in model performance…
Virtual Reality for Synergistic Surgical Training and Data Generation
Adnan Munawar, Zhaoshuo Li, Punit Kunjam +7
Surgical simulators not only allow planning and training of complex procedures, but also offer the ability to generate structured data for algorithm development, which may be appli…
Temporally Consistent Online Depth Estimation in Dynamic Scenes
Zhaoshuo Li, Wei Ye, Dilin Wang +4
Temporally consistent depth estimation is crucial for online applications such as augmented reality. While stereo depth estimation has received substantial attention as a promising…
Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detection
Weiyao Wang, Aniruddha Tamhane, Christine Santos +4
Ear related concerns and symptoms represents the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic proces…
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
Zhaoshuo Li, Nathan Drenkow, Hao Ding +5
Scene depth estimation from stereo and monocular imagery is critical for extracting 3D information for downstream tasks such as scene understanding. Recently, learning-based method…
An Interpretable Algorithm for Uveal Melanoma Subtyping from Whole Slide Cytology Images
Haomin Chen, T. Y. Alvin Liu, Catalina Gomez +2
Algorithmic decision support is rapidly becoming a staple of personalized medicine, especially for high-stakes recommendations in which access to certain information can drasticall…