arXiv subjects

Yiyue Li

Publications and source records attributed to Yiyue Li.

4 recordsLinked to original sources

arXiv2026

Improving Medical Visual Reinforcement Fine-Tuning via Perception and Reasoning Augmentation

While recent advances in Reinforcement Fine-Tuning (RFT) have shown that rule-based reward schemes can enable effective post-training for large language models, their extension to cross-modal, vision-centric domains remains largely underexplored. This limitation is especially pronounced in the medical imaging domain, where effective performance requires both robust visual perception and structured reasoning. In this work, we address this gap by proposing VRFT-Aug, a visual reinforcement fine-tuning framework tailored for the medical domain. VRFT-Aug introduces a series of training strategies designed to augment both perception and reasoning, including prior knowledge injection, perception-driven policy refinement, medically informed reward shaping, and behavioral imitation. Together, these methods aim to stabilize and improve the RFT process. Through extensive experiments across multiple medical datasets, we show that our approaches consistently outperform both standard supervised fine-tuning and RFT baselines. Moreover, we provide empirically grounded insights and practical training heuristics that can be generalized to other medical image tasks. We hope this work contributes actionable guidance and fresh inspiration for the ongoing effort to develop reliable, reasoning-capable models for high-stakes medical applications.

cs.CV↗

arXiv2025

Performance evaluation of compact plastic scintillating fiber modules for muon tomography applications

Muon tomography is a non-destructive imaging technique that exploits cosmic-ray muons from multiple directions. Its performance critically depends on the stability, active-area coverage, and spatial resolution of position-sensitive detectors. In this work, we report on the development of four compact scintillating fiber modules, each 100 cm long and composed of two staggered layers of 1 mm diameter fibers. The fibers are read out at one end by one-dimensional silicon photomultiplier arrays with a 2 mm pitch, coupled to Citiroc 1A-based front-end electronics. The modules were characterized with cosmic-ray muons, yielding a detection efficiency above 97% and a mean spatial resolution of about 0.56 mm, with uniform response over different distances from the readout end. An imaging test of a lead block was also performed, and the reconstructed results are consistent with the blocks profile. These results demonstrate the suitability of this detector design for compact and large-area systems in muon tomography applications.

physics.ins-det↗

arXiv2025

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection

Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pre-trained vision-language models, enhancing few-shot anomaly detection capabilities. However, these latest AD methods still exhibit limitations in accuracy improvement. One contributing factor is their direct comparison of a query image's features with those of few-shot normal images. This direct comparison often leads to a loss of precision and complicates the extension of these techniques to more complex domains--an area that remains underexplored in a more refined and comprehensive manner. To address these limitations, we introduce the anomaly personalization method, which performs a personalized one-to-normal transformation of query images using an anomaly-free customized generation model, ensuring close alignment with the normal manifold. Moreover, to further enhance the stability and robustness of prediction results, we propose a triplet contrastive anomaly inference strategy, which incorporates a comprehensive comparison between the query and generated anomaly-free data pool and prompt information. Extensive evaluations across eleven datasets in three domains demonstrate our model's effectiveness compared to the latest AD methods. Additionally, our method has been proven to transfer flexibly to other AD methods, with the generated image data effectively improving the performance of other AD methods.

cs.CV↗

arXiv2023

Towards a muon scattering tomography system for both low-Z and high-Z materials

Muon scattering tomography (MST) is a non-destructive technique to image various materials by utilizing cosmic ray muons as probes. A typical MST system with a two-fold track detectors is particularly effective in detecting high-$Z$ materials (e.g. nuclear materials), but difficult to recognize low-$Z$ materials (e.g. explosive materials). In this work, we present a concept of MST system to discriminate both low-$Z$ and high-$Z$ materials by extra measuring momentum of low-energy muons with a Cherenkov detector. A toy Monte Carlo simulation to describe detector responses and multiple scatterings of a muon tracking through materials is developed for statistical tests. Based on momentum-dependent track reconstruction and image reconstruction algorithm, we evaluate separation powers of different materials in the system. The results show that momentum measurement of low-energy muons and accurate track reconstruction can improve separation power of low-$Z$ materials significantly. This may enable the MST system to detect both low-$Z$ and high-$Z$ materials with cosmic ray muons in the whole energy range.

physics.ins-det↗