1 citations · 1 across the 2 of their papers we have counts for
5 papers
DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging
Adrien Meyer, Didier Mutter, Nicolas Padoy
Detecting anatomical landmarks in medical imaging is essential for diagnosis and intervention guidance. However, object detection models rely on costly bounding box annotations, li…
S4M: 4-points to Segment Anything
Adrien Meyer, Lorenzo Arboit, Giuseppe Massimiani +3
Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred boundaries make its point prompts…
UltraSam: A Foundation Model for Ultrasound using Large Open-Access Segmentation Datasets
Adrien Meyer, Aditya Murali, Farahdiba Zarin +2
Purpose: Automated ultrasound image analysis is challenging due to anatomical complexity and limited annotated data. To tackle this, we take a data-centric approach, assembling the…
CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching
Aditya Murali, Farahdiba Zarin, Adrien Meyer +3
Surgical image segmentation is highly challenging, primarily due to scarcity of annotated data. Generalist prompted segmentation models like the Segment-Anything Model (SAM) can he…
On-the-Fly Point Annotation for Fast Medical Video Labeling
Meyer Adrien, Mazellier Jean-Paul, Jeremy Dana +1
Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks w…