Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synergistic Face Detection and Pose Estimation with Energy-Based Models
The Journal of Machine Learning Research
Deep learning via semi-supervised embedding
Proceedings of the 25th international conference on Machine learning
Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms
IEEE Transactions on Information Technology in Biomedicine
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Histological analysis on stained biopsy samples requires recognizing many kinds of local and structural details, with some awareness of context. Machine learning algorithms such as convolutional networks can be powerful tools for such problems, but often there may not be enough training data to exploit them to their full potential. In this paper, we show how convolutional networks can be combined with appropriate image analysis to achieve high accuracies on three very different tasks in breast and gastric cancer grading, despite the challenge of limited training data. The three problems are to count mitotic figures in the breast, to recognize epithelial layers in the stomach, and to detect signet ring cells.