The objective of medical report generation is to convert medical images into structured text reports. The limitations of existing methods primarily include the lack of efficient local modeling and fusion algorithms, as well as severe data bias in medical datasets. To address these issues, we propose a novel method for report generation based on multi-feature fusion, comprising Anatomy-Aware Fusion Enhancement (AAFE) and Cross-modal Disease Representation Fusion (CDRF). This method first localizes specific anatomical structures and then models the interdependencies among anatomy-aware tokens through AAFE, achieving deep integration of global and anatomical tokens. Simultaneously, CDRF dynamically fuses cross-modal clinical indications, visual features, and disease states to obtain a high-order representation rich in visual and disease information, enabling the generator to generate fine-grained medical reports based on rich multi-feature embedding. Furthermore, we introduce a cross-modal cosine similarity loss mechanism to further promote the extraction of disease text features with anatomy-aware information. Experimental results on the IU-Xray and MIMIC-CXR datasets demonstrate that our method outperforms existing advanced models in the medical report generation task and exhibits outstanding performance.

When evaluating a learner's knowledge proficiency, the multiple-choice question is an efficient and widely used format in standardized tests. Nevertheless, generating these questions, particularly plausible distractors (incorrect options), poses a considerable challenge. Generally, the distractor generation can be classified into cloze-style distractor generation (CDG) and natural questions distractor generation (NQDG). In contrast to the CDG, utilizing pre-trained language models (PLMs) for NQDG presents three primary challenges: (1) PLMs are typically trained to generate “correct” content, like answers, while rarely trained to generate “plausible” content, like distractors; (2) PLMs often struggle to produce content that aligns well with specific knowledge and the style of exams; (3) NQDG necessitates the model to produce longer, context-sensitive, and question-relevant distractors. In this study, we introduce a fine-tuning framework named DGRC for NQDG in Chinese multi-choice reading comprehension from authentic examinations. DGRC comprises three major components: hard chain-of-thought, multi-task learning, and generation mask patterns. The experiment results demonstrate that DGRC significantly enhances generation performance, achieving a more than 2.5-fold improvement in BLEU scores.

Point cloud registration is a crucial challenge in photogrammetry and computer vision, aimed at aligning adjacent point clouds optimally. In this article, we present a novel registration approach based on plane constraints for large-scale LiDAR point clouds, called PCGOR, effectively decoupling rotation estimation and translation estimation. The point cloud registration challenge is then bifurcated into two distinct parts: rotation estimation and translation estimation. For rotation estimation, we develop an outlier removal method combining coarse filtering with rotation-invariant constraints (RICs) and refined filtering based on computational geometric consistency checks, effectively pruning outliers and robustly estimating accurate relative rotations from plane normals. In translation estimation, we design a componentwise approach based on translation component constraints (TCCs) to efficiently estimate relative translations. Experimental findings validate the robustness and efficacy of our proposed approach on three popular LiDAR point cloud datasets, yielding state-of-the-art performance.