33 citations · 82 across the 8 of their papers we have counts for
8 papers
Dynamic Structured Illumination Microscopy with a Neural Space-time Model
Ruiming Cao, Fanglin Linda Liu, Li-Hao Yeh +1
Structured illumination microscopy (SIM) reconstructs a super-resolved image from multiple raw images captured with different illumination patterns; hence, acquisition speed is lim…
Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI
Ruiming Cao, Xinran Zhong, Fabien Scalzo +2
Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continue…
Explanatory Graphs for CNNs
Quanshi Zhang, Xin Wang, Ruiming Cao +3
This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN. Each filter in a conv-la…
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
Quanshi Zhang, Ruiming Cao, Ying Nian Wu +1
In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized b…
Interpreting CNN Knowledge via an Explanatory Graph
Quanshi Zhang, Ruiming Cao, Feng Shi +2
This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-laye…
Interactively Transferring CNN Patterns for Part Localization
Quanshi Zhang, Ruiming Cao, Shengming Zhang +3
In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns fro…
Mining Object Parts from CNNs via Active Question-Answering
Quanshi Zhang, Ruiming Cao, Ying Nian Wu +1
Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in con…
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning
Quanshi Zhang, Ruiming Cao, Ying Nian Wu +1
This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hid…