Magnetic Resonance Imaging (MRI) is crucial for diagnosing various knee-related diseases, and developing automatic diagnostic models based on knee MRI data is highly valuable. However, this task presents significant challenges due to the need to manage MRI data with multiple sequences and numerous images, where different diseases are often associated with specific images within certain sequences. To address these challenges, we propose a multi-label classification framework designed to effectively process MRI data and handle a large-scale label space encompassing hundreds of disease categories. Our approach introduces a Slice-Level Label Attention mechanism, which enables the model to learn the alignment between labels and images within sequences, thereby enhancing both performance and interpretability. Additionally, we present a Global-Guided Attention Regularization mechanism that further improves the consistency and robustness of the Slice-Level Label Attention results. We validate our framework on a large-scale MRI dataset involving multi-label classification across hundreds of fine-grained disease categories. Experimental results demonstrate that our method not only achieves superior performance but also provides more robust and consistent interpretability.

Medical imaging plays a pivotal role in clinical diagnosis, and the textual reports associated with these images are of paramount importance in aiding image comprehension and supporting treatment decisions. Automated report generation serves to alleviate the burden on radiologists and has garnered significant attention in the field of medical artificial intelligence. Previous research in text-based report generation primarily focused on generating impressions statements from radiology findings. However, the benefits in terms of reducing the workload on radiologists were not particularly evident. In this article, we propose a novel task of generating findings from radiology impressions. Leveraging advanced large language models, we trained a set of report generation models using a real dataset of knee MRI reports. Additionally, we incorporated various strategies, including data augmentation and efficient parameter fine-tuning. Objective experiments affirm the effectiveness of the methods we introduced. Furthermore, we conducted subjective assessments by radiologists, and the results demonstrate that our trained large language models significantly outperform professional radiologists in terms of overall report quality and content consistency.