Leveraging Author-Specific Context for Scientific Figure Caption Generation: 3rd SciCap Challenge

Scientific figure captions require both accuracy and stylistic consistency to convey visual information. Here, we present a domain-specific caption generation system for the 3rd SciCap Challenge that integrates figure-related textual context with author-specific writing styles using the LaMP-Cap dataset. Our approach uses a two-stage pipeline: Stage 1 combines context filtering, category-specific prompt optimization via DSPy's MIPROv2 and SIMBA, and caption candidate selection; Stage 2 applies few-shot prompting with profile figures for stylistic refinement. Our experiments demonstrate that category-specific prompts outperform both zero-shot and general optimized approaches, improving ROUGE-1 recall by +8.3\% while limiting precision loss to -2.8\% and BLEU-4 reduction to -10.9\%. Profile-informed stylistic refinement yields 40--48\% gains in BLEU scores and 25--27\% in ROUGE. Overall, our system demonstrates that combining contextual understanding with author-specific stylistic adaptation can generate captions that are both scientifically accurate and stylistically faithful to the source paper.

SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation

SEGSRNet addresses the challenge of precisely identifying surgical instruments in low-resolution stereo endoscopic images, a common issue in medical imaging and robotic surgery. Our innovative framework enhances image clarity and segmentation accuracy by applying state-of-the-art super-resolution techniques before segmentation. This ensures higher-quality inputs for more precise segmentation. SEGSRNet combines advanced feature extraction and attention mechanisms with spatial processing to sharpen image details, which is significant for accurate tool identification in medical images. Our proposed model outperforms current models including Dice, IoU, PSNR, and SSIM, SEGSRNet where it produces clearer and more accurate images for stereo endoscopic surgical imaging. SEGSRNet can provide image resolution and precise segmentation which can significantly enhance surgical accuracy and patient care outcomes.

Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning

Claims are a fundamental unit of scientific discourse. The exponential growth in the number of scientific publications makes automatic claim extraction an important problem for researchers who are overwhelmed by this information overload. Such an automated claim extraction system is useful for both manual and programmatic exploration of scientific knowledge. In this paper, we introduce an online claim extraction system and a dataset of 1,500 scientific abstracts from the biomedical domain with expert annotations for each sentence indicating whether the sentence presents a scientific claim. We compare our proposed model with several baseline models including rule-based and deep learning techniques. Our transfer learning approach with a fine-tuning step allows us to bootstrap from a large discourse-annotated dataset (Pubmed-RCT) and obtains F1-score over 0.78 for claim detection while using a small annotated dataset of 750 papers. We show that using this pre-trained model based on the discourse prediction task improves F1-score by over 14 percent absolute points compared to a baseline model without discourse structure. We release a publicly accessible tool for discourse model, claim detection model, along with an annotation tool. We discuss further applications beyond Biomedical literature.