Semi-invertible Convolutional Neural Network for Overall Survival Prediction in Head and Neck Cancer

Saif Eddine Khelifa, Lyes Khelladi, Miloud Bagaa, Yassine Hadjadj-Aoul

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


The paper addresses the issue of overall survival prediction in head and neck cancer as an effective mean of improving clinical diagnosis and treatment planning. A new solution is proposed using semi-invertible convolutional networks. Our model exploits the 3D features of computed tomography (CT) scans to enrich the dataset used in the learning phase and thereby improve the prediction accuracy. This is achieved by designing a first architecture featuring a combination of a CNN classifier with a fully convolutional network pre-processor. The latter has been replaced in the second solution by an invertible network to deal with the memory constraints noticed in the first architecture. Obtained results showed that both architectures have led to considerable improvements in terms of prediction accuracy (0.75) compared to state-of-the-art solutions.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
Number of pages6
ISBN (Electronic)978-1-5386-8347-7
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventIEEE International Conference on Communications - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883


ConferenceIEEE International Conference on Communications
Abbreviated titleICC
Country/TerritoryKorea, Republic of


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