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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
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.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
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.
Deep Learning for Identifying Metastatic Breast Cancer
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.
Clustering Millions of Faces by Identity
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
We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images.
Ranked #15 on
Face Verification
on IJB-A
A Framework of Sparse Online Learning and Its Applications
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.
