97 citations · 108 across the 11 of their papers we have counts for
11 papers
Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology
Can Cui, Xindong Zheng, Ruining Deng +8
Anomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges. In digital pathology, ano…
Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +16
Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell s…
Nucleus subtype classification using inter-modality learning
Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios +14
Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available b…
Cell Spatial Analysis in Crohn's Disease: Unveiling Local Cell Arrangement Pattern with Graph-based Signatures
Shunxing Bao, Sichen Zhu, Vasantha L Kolachala +16
Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, part…
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
Can Cui, Yaohong Wang, Shunxing Bao +11
Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during train…
Exploring shared memory architectures for end-to-end gigapixel deep learning
Lucas W. Remedios, Leon Y. Cai, Samuel W. Remedios +8
Deep learning has made great strides in medical imaging, enabled by hardware advances in GPUs. One major constraint for the development of new models has been the saturation of GPU…
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging
Ruining Deng, Can Cui, Quan Liu +13
The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed an…
Cross-scale Multi-instance Learning for Pathological Image Diagnosis
Ruining Deng, Can Cui, Lucas W. Remedios +12
Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (M…
Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images
Ruining Deng, Can Cui, Lucas W. Remedios +12
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotation…
Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review
Can Cui, Haichun Yang, Yaohong Wang +6
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data th…
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging
Shunxing Bao, Yucheng Tang, Ho Hin Lee +8
Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing…