Jun Xu

TL;DR: A Stacked Sparse Autoencoder, an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer and out-performed nine other state of the art nuclear detection strategies.

TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.

TL;DR: A Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs) and was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP andST regions.

TL;DR: A new segmentation scheme, expectation-maximization (EM) driven geodesic active contour with overlap resolution (EMaGACOR), which is an efficient, robust, reproducible, and accurate segmentation technique that could potentially be applied to other biomedical image analysis problems.

TL;DR: This paper employs a graph-based query-specific fusion approach where multiple retrieval results are integrated and reordered based on a fused graph, capable of combining the strengths of local or holistic features adaptively for different inputs.