30 citations · 44 across the 5 of their papers we have counts for
5 papers
DIODE: Dilatable Incremental Object Detection
Can Peng, Kun Zhao, Sam Maksoud +2
To accommodate rapid changes in the real world, the cognition system of humans is capable of continually learning concepts. On the contrary, conventional deep learning models lack…
Scalable Bayesian Deep Learning with Kernel Seed Networks
Sam Maksoud, Kun Zhao, Can Peng +1
This paper addresses the scalability problem of Bayesian deep neural networks. The performance of deep neural networks is undermined by the fact that these algorithms have poorly c…
SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation
Can Peng, Kun Zhao, Sam Maksoud +2
Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will…
SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification
Sam Maksoud, Kun Zhao, Peter Hobson +2
The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems. Since mo…
CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels
Sam Maksoud, Arnold Wiliem, Kun Zhao +3
This work tackles the problem of generating a medical report for multi-image panels. We apply our solution to the Renal Direct Immunofluorescence (RDIF) assay which requires a path…