By the same authors

FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation. / Kaul, Chaitanya; Manandhar, Suresh Kumar; Pears, Nicholas Edwin.

International Symposium on Biomedical Imaging (ISBI). Venice, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Kaul, C, Manandhar, SK & Pears, NE 2019, FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation. in International Symposium on Biomedical Imaging (ISBI). Venice. <https://arxiv.org/pdf/1902.03091.pdf>

APA

Kaul, C., Manandhar, S. K., & Pears, N. E. (2019). FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation. In International Symposium on Biomedical Imaging (ISBI) https://arxiv.org/pdf/1902.03091.pdf

Vancouver

Kaul C, Manandhar SK, Pears NE. FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation. In International Symposium on Biomedical Imaging (ISBI). Venice. 2019

Author

Kaul, Chaitanya ; Manandhar, Suresh Kumar ; Pears, Nicholas Edwin. / FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation. International Symposium on Biomedical Imaging (ISBI). Venice, 2019.

Bibtex - Download

@inproceedings{9ae26959cafd4fb4938ef5467ca113f5,
title = "FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation",
abstract = "We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it{\textquoteright}s residual variant.",
keywords = "Semantic segmentation, attention in CNNs, medical imaging, U-Net, residual connections",
author = "Chaitanya Kaul and Manandhar, {Suresh Kumar} and Pears, {Nicholas Edwin}",
year = "2019",
month = apr,
language = "English",
booktitle = "International Symposium on Biomedical Imaging (ISBI)",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation

AU - Kaul, Chaitanya

AU - Manandhar, Suresh Kumar

AU - Pears, Nicholas Edwin

PY - 2019/4

Y1 - 2019/4

N2 - We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it’s residual variant.

AB - We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder. Our attention architecture is well suited for incorporation with deep convolutional networks. We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. Results show highly competitive performance when compared with U-Net and it’s residual variant.

KW - Semantic segmentation, attention in CNNs, medical imaging, U-Net, residual connections

M3 - Conference contribution

BT - International Symposium on Biomedical Imaging (ISBI)

CY - Venice

ER -