Ruining Deng

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

2023 · arXiv (Cornell University)

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific …

Fine-grained Multi-class Nuclei Segmentation with Molecular-empowered All-in-SAM Model

2025 · arXiv (Cornell University)

Purpose: Recent developments in computational pathology have been driven by advances in Vision Foundation Models, particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the …

Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification

2025 · arXiv (Cornell University)

Vision-language models (VLMs) have shown considerable potential in digital pathology, yet their effectiveness remains limited for fine-grained, disease-specific classification tasks such as distinguishing between glomerular subtypes. The subtle morphological variations among these subtypes, combined with …