Search Results for author: Dayong Wang

Found 7 papers, 2 papers with code

Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries

no code implementations4 Aug 2023 William Gao, Dayong Wang, Yi Huang

This research provides an innovative technological solution to address the long delays in metastatic breast cancer diagnosis and the consequent disparity in patient survival outcome in developing countries.

Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images

no code implementations11 Oct 2016 Manan Shah, Christopher Rubadue, David Suster, Dayong Wang

Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized regions of tissue images.

severity prediction

Deep Learning for Identifying Metastatic Breast Cancer

3 code implementations18 Jun 2016 Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Andrew H. Beck

The International Symposium on Biomedical Imaging (ISBI) held a grand challenge to evaluate computational systems for the automated detection of metastatic breast cancer in whole slide images of sentinel lymph node biopsies.

General Classification Image Classification +1

Clustering Millions of Faces by Identity

1 code implementation4 Apr 2016 Charles Otto, Dayong Wang, Anil K. Jain

Additionally, we present preliminary work on video frame clustering (achieving 0. 71 F-measure when clustering all frames in the benchmark YouTube Faces dataset).


Face Search at Scale: 80 Million Gallery

no code implementations26 Jul 2015 Dayong Wang, Charles Otto, Anil K. Jain

We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images.

Face Recognition Face Verification

A Framework of Sparse Online Learning and Its Applications

no code implementations25 Jul 2015 Dayong Wang, Pengcheng Wu, Peilin Zhao, Steven C. H. Hoi

Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification.

Anomaly Detection Classification +1

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