Search Results for author:
Found 6 papers, 3 papers with code
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection.
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation
This study investigates the impact of self-supervised pretraining of 3D semantic segmentation models on a large-scale, domain-specific dataset.
Yucca: A Deep Learning Framework For Medical Image Analysis
Medical image analysis using deep learning frameworks has advanced healthcare by automating complex tasks, but many existing frameworks lack flexibility, modularity, and user-friendliness.
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.
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language Explanations
Natural language explanations promise to offer intuitively understandable explanations of a neural network's decision process in complex vision-language tasks, as pursued in recent VL-NLE models.
Ranked #1 on
Explanation Generation
on VCR
Explain yourself! Effects of Explanations in Human-Robot Interaction
However, the results show that the robot that explains its moves is perceived as more lively and human-like.
