Artificial intelligence has demonstrated significant potential in clinical decision-making; however, developing models capable of adapting to diverse real-world scenarios and performing complex diagnostic reasoning remains a major challenge. Existing medical multi-modal benchmarks are typically limited to single-image, single-turn tasks, lacking multi-modal medical image integration and failing to capture the longitudinal and multi-modal interactive nature inherent to clinical practice. To address this gap, we introduce MedAtlas, a novel benchmark framework designed to evaluate large language models on realistic medical reasoning tasks. MedAtlas is characterized by four key features: multi-round visual question answering (VQA), Joint reasoning of multiple modalities of medical images, multi-task integration, and high clinical fidelity. It supports four core tasks: open-ended multi-round VQA, closed-ended multi-round VQA, multi-image joint reasoning, and comprehensive disease diagnosis. Each case is derived from real diagnostic workflows and incorporates temporal interactions between textual medical histories and multiple imaging modalities, including CT, MRI, PET, ultrasound, X-ray, etc., requiring models to perform deep integrative reasoning across images and clinical texts. MedAtlas provides expert-annotated gold standards for all tasks. Furthermore, we propose two novel evaluation metrics: Stage Chain Accuracy (SCA) and Error Propagation Suppression Coefficient (EPSC). Benchmark results with existing multi-modal models reveal substantial performance gaps in multi-stage clinical reasoning. MedAtlas establishes a challenging evaluation platform to advance the development of robust and trustworthy medical AI.

Landmark detection is essential in medical image analysis, aiding tasks like surgical navigation, diagnosis, and treatment planning. However, it remains challenging due to the need for fine-grained local detail and long-range spatial dependency modeling in high-dimensional volumetric data. Existing approaches struggle to balance accuracy, efficiency, and robustness, especially in cases of sparse landmark distribution, anatomical variability, and noisy or incomplete scans. We propose H3DE-Net, a hybrid framework combining CNNs for local feature extraction and a lightweight transformer-based attention module for global context. A volumetric bi-level routing attention mechanism reduces computational overhead while preserving long-range dependencies, and multi-scale feature fusion enhances precision and robustness. This design integrates both global and local representations, overcoming the limitations of CNN-only or transformer-only models. Extensive experiments on a public CT dataset show that H3DE-Net achieves state-of-the-art performance, significantly improving mean radial error (MRE) and success detection rate (SDR) compared to existing methods. The model is robust in challenging scenarios with missing landmarks or anatomical variations, demonstrating its applicability in real-world clinical settings. All code, pretrained weights, and data processing scripts are publicly available for reproducibility and further research.