MediSee: Reasoning-based Pixel-level Perception in Medical Images
This work addresses the challenge of making medical image analysis more accessible to general users by reducing reliance on domain-specific knowledge, though it is incremental as it builds on existing segmentation and detection techniques.
The paper tackles the problem of medical image perception by introducing a new task, Medical Reasoning Segmentation and Detection (MedSD), which uses implicit oral queries requiring logical reasoning instead of specialized inputs like bounding boxes, and proposes the MediSee model that outperforms traditional methods on this task.
Despite remarkable advancements in pixel-level medical image perception, existing methods are either limited to specific tasks or heavily rely on accurate bounding boxes or text labels as input prompts. However, the medical knowledge required for input is a huge obstacle for general public, which greatly reduces the universality of these methods. Compared with these domain-specialized auxiliary information, general users tend to rely on oral queries that require logical reasoning. In this paper, we introduce a novel medical vision task: Medical Reasoning Segmentation and Detection (MedSD), which aims to comprehend implicit queries about medical images and generate the corresponding segmentation mask and bounding box for the target object. To accomplish this task, we first introduce a Multi-perspective, Logic-driven Medical Reasoning Segmentation and Detection (MLMR-SD) dataset, which encompasses a substantial collection of medical entity targets along with their corresponding reasoning. Furthermore, we propose MediSee, an effective baseline model designed for medical reasoning segmentation and detection. The experimental results indicate that the proposed method can effectively address MedSD with implicit colloquial queries and outperform traditional medical referring segmentation methods.