Most retrieval-based report generation methods rely on sentence-level templates, which often introduce ambiguities due to similar semantics across different sentences. To overcome this, we propose a phrase-level framework, comprising automatic phrase template extraction and report generation based on retrieval. In the first stage, we introduce a phrase scoring mechanism to evaluate the semantics and importance of phrases, enabling efficient template extraction. In the second stage, we retrieve relevant templates and fuse their features with visual features from the radiograph through a Retrieval-Aggregation strategy. The dynamic update of the template bank during training improves template representations. Experiments on IU X-Ray and MIMIC-CXR datasets demonstrate the effectiveness of our method in generating accurate radiology reports.

The increasing interconnectivity of HPC systems has highlighted the need for efficient application migration across different environments. Containers, widely adopted for this purpose, simplify deployment but often fail to deliver optimal performance due to the separated build and execution container workflow. This leads to generic container images that miss out on system-specific software stack advantages, a challenge we define as the adaptability issue.

Medical reports play a pivotal role in achieving accurate diagnoses. This technology not only reduces the burden on radiologists but also fosters consistency in treatment approaches. The crux of generating high-caliber medical reports lies in the model’s ability to interpret and integrate both visual and textual data. However, the inherent distributional disparities between modalities pose a significant challenge to this process. Hence, we propose UEMA framework, which extracts features from both modalities through a Unified Encoder and consists of an ICCL (Inter-Case Contrastive Learning) module to facilitate multimodal alignment. The ICCL module leverages multi-label contrastive learning across different cases to align visual and textual features. Extensive experiments have been conducted on the publicly available IU X-Ray and MIMIC-CXR datasets with additional case studies and visual analysis, demonstrating the effectiveness of our designed module and that our model outperforms state-of-the-art methods across a wide range of metrics.