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Enhancing LLMs with LoRA Fine-Tuning Using Medical Data and Knowledge Graph Enrichment for Improved Healthcare Outcomes
(IEEE, 2025-06-02)
Jankov, A.
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This research paper investigates the enhancement of large language models (LLMs) within the medical domain, focusing on members of the Llama family of LLMs. While LLMs have demonstrated remarkable success across various general-purpose natural language processing tasks, their application in specialized domains like medicine is often hindered by limited training on domain-specific data, resulting in suboptimal accuracy and contextual relevance. To address these limitations, this research employs low-rank adaptation (LoRA) to fine-tune LLMs on real-world patientphysician dialogues, effectively capturing the intricacies of medical discourse. Additionally, the knowledge of the LLM is enriched with the SPOKE knowledge graph, a structured repository of medical domain information, allowing the model to generate outputs that are both contextually and scientifically grounded. The experimental results underscore the transformative impact of this dual approach, demonstrating significant advancements in tasks such as automatic diagnosis generation and personalized drug recommendation. However, this research should be viewed as an exploratory proof of concept. Significant limitations, including the constrained evaluation scope and the critical need for expert clinical validation and thorough ethical review, must be addressed in future work before considering real-world applicability.
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Few-Shot Semantic Segmentation in Remote Sensing: A Review on Definitions, Methods, Datasets, Advances and Future Trends
(MDPI AG, 2026-02-18)
Petrov, Marko
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Pandilova, Ema
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Semantic segmentation in remote sensing images, which is the task of classifying each pixel of the image in a specific category, is widely used in areas such as disaster management, environmental monitoring, precision agriculture, and many others. However, traditional semantic segmentation methods face a major challenge: they require large amounts of annotated data to train effectively. To tackle this challenge, few-shot semantic segmentation has been introduced, where the models can learn and adapt quickly to new classes from just a few annotated samples. This paper presents a comprehensive review of recent advances in few-shot semantic segmentation (FSSS) for remote sensing, covering datasets, methods, and emerging research directions. We first outline the fundamental principles of few-shot learning and summarize commonly used remote-sensing benchmarks, emphasizing their scale, geographic diversity, and relevance to episodic evaluation. Next, we categorize FSSS methods into major families (meta-learning, conditioning-based, and foundation-assisted approaches) and analyze how architectural choices, pretraining strategies, and inference protocols influence performance. The discussion highlights empirical trends across datasets, the behavior of different conditioning mechanisms, the impact of self-supervised and multimodal pretraining, and the role of reproducibility and evaluation design. Finally, we identify key challenges and future trends, including benchmark standardization, integration with foundation and multimodal models, efficiency at scale, and uncertainty-aware adaptation. Collectively, they signal a shift toward unified, adaptive models capable of segmenting novel classes across sensors, regions, and temporal domains with minimal supervision.

Faculty of Computer Science and Engineering

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