MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities

TL;DR

MedROV is a real-time open-vocabulary detection model for medical imaging that leverages large-scale datasets, pseudo-labeling, and foundation models to detect known and novel structures across multiple modalities.

Contribution

We introduce MedROV, the first real-time open-vocabulary detection model for medical imaging, with a large-scale dataset and novel training strategies to handle diverse modalities and unseen objects.

Findings

Outperforms previous foundation models by 40 mAP50

Surpasses closed-set detectors by over 3 mAP50

Operates at 70 FPS in real-time

Abstract

Traditional object detection models in medical imaging operate within a closed-set paradigm, limiting their ability to detect objects of novel labels. Open-vocabulary object detection (OVOD) addresses this limitation but remains underexplored in medical imaging due to dataset scarcity and weak text-image alignment. To bridge this gap, we introduce MedROV, the first Real-time Open Vocabulary detection model for medical imaging. To enable open-vocabulary learning, we curate a large-scale dataset, Omnis, with 600K detection samples across nine imaging modalities and introduce a pseudo-labeling strategy to handle missing annotations from multi-source datasets. Additionally, we enhance generalization by incorporating knowledge from a large pre-trained foundation model. By leveraging contrastive learning and cross-modal representations, MedROV effectively detects both known and novel structures. Experimental results demonstrate that MedROV outperforms the previous state-of-the-art foundation model for medical image detection with an average absolute improvement of 40 mAP50, and surpasses closed-set detectors by more than 3 mAP50, while running at 70 FPS, setting a new benchmark in medical detection. Our source code, dataset, and trained model are available at https://github.com/toobatehreem/MedROV.