2 citations · 4 across the 6 of their papers we have counts for
6 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…
Spatially-Adaptive Gradient Re-parameterization for 3D Large Kernel Optimization
Ho Hin Lee, Quan Liu, Shunxing Bao +2
Large kernel convolutions offer a scalable alternative to vision transformers for high-resolution 3D volumetric analysis, yet naively increasing kernel size often leads to optimiza…
Influence of Early through Late Fusion on Pancreas Segmentation from Imperfectly Registered Multimodal MRI
Lucas W. Remedios, Han Liu, Samuel W. Remedios +7
Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse inf…
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…
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning
Can Cui, Ruining Deng, Quan Liu +5
The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach. With the zero-shot segmentation capacity, SAM…
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…