Search Results for author:
Found 3 papers, 1 papers with code
DeViL: Decoding Vision features into Language
We demonstrate that DeViL generates textual descriptions relevant to the image content on CC3M surpassing previous lightweight captioning models and attribution maps uncovering the learned concepts of the vision backbone.
An Efficient Anchor-free Universal Lesion Detection in CT-scans
Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions.
Ranked #6 on
Medical Object Detection
on DeepLesion
DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection
In this paper, we exploit the domain information present in computed tomography (CT) scans and propose a robust universal lesion detection (ULD) network that can detect lesions across all organs of the body by training on a single dataset, DeepLesion.
Ranked #2 on
Medical Object Detection
on DeepLesion
