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[2602.21657] Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis
Summary
The paper presents VCC-Net, a visual cognition-guided cooperative network aimed at enhancing chest X-ray diagnosis through improved human-AI collaboration and interpretability.
Why It Matters
This research addresses critical gaps in computer-aided diagnosis by integrating radiologists' visual cognition into AI models, potentially increasing diagnostic accuracy and clinical adoption. The findings could lead to more reliable AI systems in healthcare, ultimately improving patient outcomes.
Key Takeaways
- VCC-Net enhances chest X-ray diagnosis by incorporating radiologists' visual cognition.
- The model achieves high classification accuracies on multiple datasets.
- Attention maps from VCC-Net align with radiologists' gaze patterns, reinforcing model reliability.
- The integration of visual cognition helps mitigate bias in AI decision-making.
- The approach promotes a transparent and collaborative diagnostic process.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.21657 (cs) [Submitted on 25 Feb 2026] Title:Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis Authors:Shaoxuan Wu, Jingkun Chen, Chong Ma, Cong Shen, Xiao Zhang, Jun Feng View a PDF of the paper titled Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosis, by Shaoxuan Wu and 5 other authors View PDF HTML (experimental) Abstract:Computer-aided diagnosis (CAD) has significantly advanced automated chest X-ray diagnosis but remains isolated from clinical workflows and lacks reliable decision support and interpretability. Human-AI collaboration seeks to enhance the reliability of diagnostic models by integrating the behaviors of controllable radiologists. However, the absence of interactive tools seamlessly embedded within diagnostic routines impedes collaboration, while the semantic gap between radiologists' decision-making patterns and model representations further limits clinical adoption. To overcome these limitations, we propose a visual cognition-guided collaborative network (VCC-Net) to achieve the cooperative diagnostic paradigm. VCC-Net centers on visual cognition (VC) and employs clinically compatible interfaces, such as eye-tracking or the mouse, to capture radiologists' visual search traces and attention patterns during diagnosis. VCC-Net employs VC as a spatial cognition guide, learning hierarchica...