2017
Akbar, Shazia; Peikari, Mohammad; Salama, Sherine; Nofech-Mozes, Sharon; Martel, Anne L.
Transitioning between Convolutional and Fully Connected Layers in Neural Networks Proceedings Article
In: 3rd workshop on Deep Learning in Medical Image Analysis (DLMIA), MICCAI 2017, 2017.
Abstract | Links | BibTeX | Tags: CBCF-DCIS
@inproceedings{Akbar2017a,
title = {Transitioning between Convolutional and Fully Connected Layers in Neural Networks},
author = {Shazia Akbar and Mohammad Peikari and Sherine Salama and Sharon Nofech-Mozes and Anne L. Martel},
url = {https://arxiv.org/abs/1707.05743},
year = {2017},
date = {2017-00-00},
booktitle = {3rd workshop on Deep Learning in Medical Image Analysis (DLMIA), MICCAI 2017},
abstract = {Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified "transition" module which learns global average pooling layers from filters of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.},
keywords = {CBCF-DCIS},
pubstate = {published},
tppubtype = {inproceedings}
}
Digital pathology has advanced substantially over the last decade however tumor localization continues to be a challenging problem due to highly complex patterns and textures in the underlying tissue bed. The use of convolutional neural networks (CNNs) to analyze such complex images has been well adopted in digital pathology. However in recent years, the architecture of CNNs have altered with the introduction of inception modules which have shown great promise for classification tasks. In this paper, we propose a modified "transition" module which learns global average pooling layers from filters of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.