6 papers
Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt
Joongwon Chae, Lihui Luo, Xi Yuan +4
Accurate tongue segmentation is crucial for reliable TCM analysis. Supervised models require large annotated datasets, while SAM-family models remain prompt-driven. We present Memo…
In-batch Relational Features Enhance Precision in An Unsupervised Medical Anomaly Detection Task
P. Bilha Githinji, Ijaz Gul, Lian Zhang +3
Confounding pathology with normal anatomical variation remains a significant challenge in unsupervised medical-image anomaly detection, resulting in numerous false positives. To en…
Mapping the maturation of TCM as an adjuvant to radiotherapy
P. Bilha Githinji, Aikaterini Melliou, Xi Yuan +8
The integration of complementary medicine into oncology represents a paradigm shift that has seen to increasing adoption of Traditional Chinese Medicine (TCM) as an adjuvant to rad…
StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection
Joongwon Chae, Lihui Luo, Yang Liu +8
Max pooling is the de facto standard for converting anomaly score maps into image-level decisions in memory-bank-based unsupervised anomaly detection (UAD). However, because it rel…
GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection
Joongwon Chae, Lihui Luo, Yang Liu +8
Feature-based anomaly detection is widely adopted in industrial inspection due to the strong representational power of large pre-trained vision encoders. While most existing method…
HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation
Haoxuan Li, Wei song, Peiwu Qin +2
Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these challenges, this study proposes a…