35 citations · 193 across the 15 of their papers we have counts for
32 papers · 1 filter
Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning
Yilin Wang, Shaozuo Yu, Xiaokang Yang +1
In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy. We achiev…
Glance-and-Gaze Vision Transformer
Qihang Yu, Yingda Xia, Yutong Bai +3
Recently, there emerges a series of vision Transformers, which show superior performance with a more compact model size than conventional convolutional neural networks, thanks to t…
Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
Yan Wang, Peng Tang, Yuyin Zhou +3
Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segment…
Batch Normalization with Enhanced Linear Transformation
Yuhui Xu, Lingxi Xie, Cihang Xie +5
Batch normalization (BN) is a fundamental unit in modern deep networks, in which a linear transformation module was designed for improving BN's flexibility of fitting complex data…
Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples
Yingwei Li, Zhuotun Zhu, Yuyin Zhou +4
Although deep neural networks have been a dominant method for many 2D vision tasks, it is still challenging to apply them to 3D tasks, such as medical image segmentation, due to th…
CO2: Consistent Contrast for Unsupervised Visual Representation Learning
Chen Wei, Huiyu Wang, Wei Shen +1
Contrastive learning has been adopted as a core method for unsupervised visual representation learning. Without human annotation, the common practice is to perform an instance disc…