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Yiliu Xu

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Open access Jul 2026

BAR-UNet: a boundary-aware and appearance-robust framework for polyp segmentation

Background Colorectal cancer is a leading cause of cancer-related mortality, and reliable polyp segmentation during colonoscopy is critical for early intervention. Existing deep learning segmentors often produce blurred boundaries and are sensitive to appearance variation across endoscopy devices. Methods We propose BAR-UNet, a ResNet-34 encoder–decoder with a boundary-aware head and mask-guided appearance consistency learning (MACL). On the public Kvasir-SEG dataset we compare BAR-UNet with classical semantic segmentors (FCN-ResNet50, DeepLabV3-ResNet50), detection-based segmentors (YOLOv8-Seg, YOLOv11-Seg, Mask R-CNN), polyp-specific CNN baselines, and modern enhanced segmentors (DeepLabV3+, SegFormer-B2, MedSAM, SAM-Med2D) under a unified 70/10/20 split, multi-seed evaluation protocol. Results BAR-UNet achieves Dice 0.8798 ±0.0024 and IoU 0.7913 ± 0.0029 on Kvasir-SEG, outperforming all compared methods. Zero-shot evaluation on CVC-ClinicDB, CVC-ColonDB, and ETIS-Larib yields Dice/IoU of 0.831/0.748, 0.776/0.682, and 0.728/0.631, respectively. Ablation studies, boundary metrics (F1^bd, MAE, HD95), paired Wilcoxon tests, and MACL sensitivity analyses confirm that the boundary head and MACL are complementary. Discussion BAR-UNet improves boundary precision and appearance robustness with modest computational overhead on an NVIDIA RTX 4060 GPU. The method is a promising research prototype for computer-aided polyp analysis; prospective clinical validation is required before deployment.

Yiliu Xu, Lingling Liu, Meiwen Tang · Frontiers in Oncology · 0 citations

Preprint Jul 2026

Chain-Aware Encoding for Microservice Trace Anomaly Detection

Microservice traces can be structurally anomalous even when every span returns normally -- a payment flow that silently skips a risk check looks fine to any per-span monitor. Sequence models like DeepLog address this by predicting the next event, but they treat each API endpoint as a context-free token: the same endpoint reached through different invocation chains is mapped to the same vocabulary entry, even when its normal behavior differs across contexts. We propose encoding each event as an (endpoint, root-to-span invocation chain) pair instead. This simple change has two consequences: unseen chains are flagged without model inference, and next-event predictions become context-conditional, turning subtle path anomalies into clear outliers. We instantiate this idea in CHAINLSTM, a lightweight dual-task LSTM supporting per-event online detection. On the TrainTicket benchmark, CHAINLSTM achieves 94.3% F1 (+5.3 pp over DeepLog) with comparable latency recall and 99.1\% path recall. Case analysis shows that chain-aware encoding shifts median prediction probability on path anomalies from 0.91 to 0.002, suggesting a wider separation margin for threshold-based detection.

Yiliu Xu, Ziwei Hong, Zhongheng Yang et al. · 3 citations