TY - GEN
T1 - Deep Learning for Tumor Margin Identification in Electromagnetic Imaging
AU - Mirbeik, Amir
AU - Tavassolian, Negar Ebadi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper proposes a deep learning technique which accurately divides the electromagnetic images of cancer-affected tissues into two regions of tumorous and normal sections. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior to or during the excision surgery. We formulate deep learning from a perspective relevant to electromagnetic image reconstruction. A recurrent auto-encoder network architecture is presented. The effectiveness of the algorithm is demonstrated by segmenting the reconstructed images of an experimental tissue-mimicking phantom. The structure similarity measure (SSIM) and mean-square-error (MSE) of the reconstructed result are approximately 0.94 and 0.04 respectively, while the values obtained from conventional frequency-domain reconstruction methods can hardly overcome 0.35 and 0.41 respectively.
AB - This paper proposes a deep learning technique which accurately divides the electromagnetic images of cancer-affected tissues into two regions of tumorous and normal sections. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior to or during the excision surgery. We formulate deep learning from a perspective relevant to electromagnetic image reconstruction. A recurrent auto-encoder network architecture is presented. The effectiveness of the algorithm is demonstrated by segmenting the reconstructed images of an experimental tissue-mimicking phantom. The structure similarity measure (SSIM) and mean-square-error (MSE) of the reconstructed result are approximately 0.94 and 0.04 respectively, while the values obtained from conventional frequency-domain reconstruction methods can hardly overcome 0.35 and 0.41 respectively.
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U2 - 10.1109/USNC-URSI52151.2023.10237765
DO - 10.1109/USNC-URSI52151.2023.10237765
M3 - Conference contribution
AN - SCOPUS:85172412365
T3 - IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)
SP - 1873
EP - 1874
BT - 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings
T2 - 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023
Y2 - 23 July 2023 through 28 July 2023
ER -