126 citations · 734 across the 65 of their papers we have counts for
9 papers · 1 filter
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…