Yuankai Huo

Multi-modal learning with missing data for cancer diagnosis using histopathological and genomic data

2022 · Medical Imaging 2022: Computer-Aided Diagnosis

Multi-modal learning (e.g., integrating pathological images with genomic features) tends to improve the accuracy of cancer diagnosis and prognosis as compared to learning with a single modality. However, missing data is a common problem in …

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 …