Feature Decomposition via Shared Low-Rank Matrix Recovery for CT Report Generation

Generating reports for medical images is an important task in medical automation that not only provides valuable objective diagnostic evidence but also alleviates the workload of radiologists. Many existing studies focus on chest X-rays that typically consist of one or a few images, where less attention is paid to other medical image types, such as computed tomography (CT) that contain a large number of continuous images. Many studies on CT report generation (CTRG) rely on convolutional networks or standard Transformers to model CT slice representation and combine them to obtain CT features, yet relatively little research has focused on subtle lesion features and volumetric continuity. In this paper, we propose shared low-rank matrix recovery (S-LMR) to decompose CT slices into shared anatomical patterns and lesion-focused features, together with continuous slice encoding (CSE) to explicitly model inter-slice continuity and capture progressive changes across adjacent slices, which are subsequently integrated with a large language model (LLM) for report generation. Specifically, the S-LMR separates the common patterns from the sparse lesion-focused features to highlight clinically significant information. Based on the outputs of S-LMR, CSE captures inter-slice relationships within a dedicated Transformer encoder and aligns the resulting visual features with textual information, thereby instructing the LLM to produce a CT report. Experiment results on benchmark datasets for CTRG show that our approach outperforms strong baselines and existing models, demonstrating state-of-the-art performance. Analyses further confirm that S-LMR and CSE effectively capture key evidence, leading to more accurate CTRG.