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Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time- and cost-consuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable morphological features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CAT-Net can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CAT-Net, captures morphological patterns of interest in hierarchical levels in the model. The proposed method out-performed the current state-of-the-art benchmark methods on accuracy, precision, recall, and F1 score.
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Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses
The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology’s promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.
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- PAR ID:
- 10114213
- Date Published:
- Journal Name:
- Proceedings of WWW 2019 Conference
- Page Range / eLocation ID:
- 2068 to 2079
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3308558.3313713
