Search Results for author:
Found 11 papers, 1 papers with code
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms
A comprehensive understanding of surgical scenes allows for monitoring of the surgical process, reducing the occurrence of accidents and enhancing efficiency for medical professionals.
Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited.
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health.
Shortcut Learning in Medical Image Segmentation
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set.
S^2Former-OR: Single-Stage Bimodal Transformer for Scene Graph Generation in OR
In this study, we introduce a novel single-stage bimodal transformer framework for SGG in the OR, termed S^2Former-OR, aimed to complementally leverage multi-view 2D scenes and 3D point clouds for SGG in an end-to-end manner.
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements.
An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health.
Removing confounding information from fetal ultrasound images
In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.
I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks
Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts.
DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation
We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset.
diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure.
