Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation

• Published in: IEEE transactions on image processing Vol. 27; no. 5; pp. 2189 - 2200
• Main Authors: Saha, Monjoy, Chakraborty, Chandan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2018
• Subjects: Biomembranes; Breast - chemistry; Breast - cytology; Breast - diagnostic imaging; Breast cancer; Breast Neoplasms - chemistry; Breast Neoplasms - diagnostic imaging; cell membrane; Cell Membrane - chemistry; Cell Membrane - classification; Cell Nucleus - chemistry; Cell Nucleus - classification; Deep Learning; Female; Fish; HER2; Histocytochemistry; Humans; Image color analysis; Image Interpretation, Computer-Assisted - methods; Image segmentation; LSTM; Machine learning; nuclei; Receptor, ErbB-2; Software

We present an efficient deep learning framework for identifying, segmenting, and classifying cell membranes and nuclei from human epidermal growth factor receptor-2 (HER2)-stained breast cancer images with minimal user intervention. This is a long-standing issue for pathologists because the manual q...