Search Results for author: Ruiming Cao

Found 8 papers, 0 papers with code

Dynamic Structured Illumination Microscopy with a Neural Space-time Model

no code implementations3 Jun 2022 Ruiming Cao, Fanglin Linda Liu, Li-Hao Yeh, Laura Waller

We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution.


Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI

no code implementations5 Oct 2019 Ruiming Cao, Xinran Zhong, Fabien Scalzo, Steven Raman, Kyung hyun Sung

Here, we propose the baseline MRI model to alternatively learn the appearance of mp-MRI using radiology-confirmed negative MRI cases via weakly supervised learning.

Management Weakly-supervised Learning

Explanatory Graphs for CNNs

no code implementations18 Dec 2018 Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu

This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.

Interactively Transferring CNN Patterns for Part Localization

no code implementations5 Aug 2017 Quanshi Zhang, Ruiming Cao, Shengming Zhang, Mark Redmonds, Ying Nian Wu, Song-Chun Zhu

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 from noisy signals.

Interpreting CNN Knowledge via an Explanatory Graph

no code implementations5 Aug 2017 Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu

Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph.

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

no code implementations14 Nov 2016 Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu

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 hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding.

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