35 citations · 63 across the 4 of their papers we have counts for
4 papers
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Jun Ma, Feifei Li, Sumin Kim +79
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive co…
Taming Mambas for Voxel Level 3D Medical Image Segmentation
Luca Lumetti, Vittorio Pipoli, Kevin Marchesini +3
Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each w…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
M-VAD Names: a Dataset for Video Captioning with Naming
Stefano Pini, Marcella Cornia, Federico Bolelli +2
Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic "someone" tag. The lack of movie description d…