IME MS Paper X Fu 2020 Recurrence Network Analysis of IDC Yang

Abstract— Invasive ductal carcinoma (IDC) is the most common subtype of all Breast Cancers (BC). The histopathological image analysis is one of the most crucial diagnostic procedures to identify IDC. However, this image diagnosis process is currently time-consuming and heavily dependent on human expertise, making the IDC detection a bottleneck of the BC treatment. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for improving the detection performance. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. After segmenting the Whole Slide Images (WSI) into patches, we first utilize the two-level wavelet decomposition to filter and delineate the hidden information in the images. Then, we model the decomposed patches with a recurrence network approach to characterize and quantify the recurrence patterns of the images. Finally, with machine learning techniques, we develop machine learning models to automate IDC detection with recurrence features extracted from the images. The machine learning models achieve the performances of at least AUC = 0.96, which is comparable to an experienced histopathologist.

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Work Title IME MS Paper X Fu 2020 Recurrence Network Analysis of IDC Yang
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Penn State
Creators
  1. Xuanya Fu
Keyword
  1. Breast Cancer
  2. recurrence analysis
  3. invasive ductal carcinoma
  4. weighted recurrence network
  5. whole-slide image
License All rights reserved
Work Type Research Paper
Deposited July 07, 2020

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