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16.07 - An N-Point Statistics Framework for Predicting Tissue Traits in Biomedical Images

Project - Summary


To develop a general biomedical image informatics framework that is capable of predicting the traits of tissues captured in images of immunohistochemistry-stained (IHC) specimens. Our work on the framework has been on-going for a number of years. The CVDI Year 5 funding supported three separate sub-projects that contributed to the enhancement of the framework. They are:

  • Definition of an alternate color representation for the analysis of Hematoxylin and Eosin (H&E) images (a type of IHC image), which supports color normalization that effectively reduces inter-slide variability during downstream analysis.
  • Estimation of fine-scale histologic features at low magnification which allows for the analysis of IHC images at lower pixel resolutions; thus providing computational speed-ups that facilitate high-throughput analysis of while-slide IHC images.
  • Development of an approach for characterization spatial structures in biomedical images based on N-point statistics analysis. The features derived from the analysis may be used to predict tissue traits.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
David Breen PI Not available Not available Drexel University
Mark Zarella PI Not available Not available Drexel University
Chan Yeoh Student Not available Not available Drexel University
Matthew Quaschnick Student Not available Not available Drexel University
Dan Johnson Student Not available Not available Drexel University
Zahra Riahi Samani Visiting PhD Student Not available Not available Shahid Beheshti University (Iran)

Project - Impact and Uses/Benefits

The contributions of our research are thus three-fold: (1) We propose four incremental learning algorithms that can update the decision boundary for the new incoming samples of the classifier without retraining the classifier with the entire training set each time. Moreover, one of the methods, IPAP, can also update the decision for original data (2) We proposed a customized incremental learning algorithm (Incremental High precision rule base Naive Bayesian Classifier) to update bibliographic datasets without the need for labeled information. The disambiguation results from an existing disambiguation system as also optional in our framework. The high-precision rule NBC algorithm demonstrates high precision on a large-scale name block comprising nearly 20,000 citation records. To our knowledge, this test dataset is one of the largest name blocks ever used in an incremental author disambiguation evaluation research project. (3) We successfully benchmark the performance of our proposed methods against other incremental approaches, where we demonstrate notable improvements.

Project - Deep Dive

Project - Documents

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projects/year5/16.07.1566398418.txt.gz · Last modified: 2019/08/21 09:40 by sally.johnson