loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Yanbo Feng ; Adel Hafiane and Hélène Laurent

Affiliation: INSA CVL, Laboratoire PRISME, Bourges, France

Keyword(s): Feature Map, Convolutional Neural Network, Weakly Supervised Learning, Image Processing, Histopathological Image.

Abstract: Feature map is obtained from the middle layer of convolutional neural network (CNN), it carries the regional information captured by network itself about the target of input image. This property is widely used in weakly supervised learning to achieve target localization and segmentation. However, the traditional method of processing feature map is often associated with the weight of output layer. In this paper, the weak correlation between feature map and weight is discussed. We believe that it is not accurate to directly transplant the weights of output layer to feature maps, the reason is that the global mean value of feature map loses its spatial information, weighting scalars cannot accurately constrain the three-dimensional feature maps. We highlight that the feature map in a specific channel has invariance to target’s location, it can stably activate the more complete region directly related to target, that is, the feature map ability has strong correlation with the channel.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.200.94.150

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Feng, Y.; Hafiane, A. and Laurent, H. (2022). Weakly Supervised Segmentation of Histopathology Images: An Insight in Feature Maps Ability for Learning Models Interpretation. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 421-427. DOI: 10.5220/0010830400003124

@conference{visapp22,
author={Yanbo Feng. and Adel Hafiane. and Hélène Laurent.},
title={Weakly Supervised Segmentation of Histopathology Images: An Insight in Feature Maps Ability for Learning Models Interpretation},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={421-427},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010830400003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Weakly Supervised Segmentation of Histopathology Images: An Insight in Feature Maps Ability for Learning Models Interpretation
SN - 978-989-758-555-5
IS - 2184-4321
AU - Feng, Y.
AU - Hafiane, A.
AU - Laurent, H.
PY - 2022
SP - 421
EP - 427
DO - 10.5220/0010830400003124
PB - SciTePress