Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evalu

by   Md Sakib Hossain Shovon, et al.
Southern Illinois University
Design and Development by:

Breast Cancer (BC) is among women's most lethal health concerns. Early diagnosis can alleviate the mortality rate by helping patients make efficient treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become one the most lethal subtype of BC. According to the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO), the severity level of HER2 expression can be classified between 0 and 3+ range. HER2 can be detected effectively from immunohistochemical (IHC) and, hematoxylin & eosin (HE) images of different classes such as 0, 1+, 2+, and 3+. An ensemble approach integrated with threshold filtered single instance evaluation (SIE) technique has been proposed in this study to diagnose BC from the multi-categorical expression of HER2 subtypes. Initially, DenseNet201 and Xception have been ensembled into a single classifier as feature extractors with an effective combination of global average pooling, dropout layer, dense layer with a swish activation function, and l2 regularizer, batch normalization, etc. After that, extracted features has been processed through single instance evaluation (SIE) to determine different confidence levels and adjust decision boundary among the imbalanced classes. This study has been conducted on the BC immunohistochemical (BCI) dataset, which is classified by pathologists into four stages of HER2 BC. This proposed approach known as DenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all other existing state-of-art models with an accuracy of 97.12%, precision of 97.15%, and recall of 97.68% on H&E data and, accuracy of 97.56%, precision of 97.57%, and recall of 98.00% on IHC data respectively, maintaining momentous improvement. Finally, Grad-CAM and Guided Grad-CAM have been employed in this study to interpret, how TL-based model works on the histopathology dataset and make decisions from the data.


page 1

page 4

page 5

page 8

page 9

page 13

page 14


Ensemble classifier approach in breast cancer detection and malignancy grading- A review

The diagnosed cases of Breast cancer is increasing annually and unfortun...

Deep Learning Based Model for Breast Cancer Subtype Classification

Breast cancer has long been a prominent cause of mortality among women. ...

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

The evaluation of human epidermal growth factor receptor 2 (HER2) expres...

Using Machine Learning to Automate Mammogram Images Analysis

Breast cancer is the second leading cause of cancer-related death after ...

Please sign up or login with your details

Forgot password? Click here to reset