Research Interest

• Radiomics & multi-omics

• Medical image analysis

• Biomedical engineering 

• Signal and image processing 

• Machine learning-Deep learning 

• Computer assited diagnosis

• Information theory

• Computer vision

Personal Research links

• Google Scholar: https://scholar.google.com/citations?hl=en&user=ACaSfwoAAAAJ&view_op=list_works&sortby=pubdate

• https://orcid.org/0000-0003-3402-9576

• Scopus: https://www.scopus.com/authid/detail.uri?authorId=53982608400

Experience

• 2020-ongoing: Professor, School of artificial Intelligence, GUET, Guilin, Guangxi, China

• 2018-2020 (2 years): Project director, Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada

• 2017-2019 (2 years): Adjunct Professor, Ecole de Technologie Superieure (ETS), Montreal, QC, Canada

• 2015-2017 (2 years): Post-doc, McGill University & ETS, Montreal, QC, Canada

• 2013-2015 (18 months): Post-doc, University of Texas MD Anderson Cancer Centre, Houston, TX, USA

• 2013-2013 (7 months): Post-doc, Villanova University, Villanova, PA, USA

• 2012-2013 (6 months): Research Associate, Ecole Polytechnique de Montreal, Montreal, QC, Canada

Education

• 2009-2012: PhD., Engineering Systems, University of Lorraine, Metz, France

• 2007-2008: Master-DEA., Bio-mechanical & Biomedical Engineering, University of Technology of Compiegne, Compiegne, France

• 2002-2007: B. Eng., Biomedical Engineering, Islamic University of Lebanon, Beirut-Khalde, Lebanon

Peer-reviewed article - journals 

1. Chaddad A., Tanougast C., 2023  “CNN approach for predicting survival outcome of patients with COVID-19”, IEEE Internet of Things, DOI: 10.1109/JIOT.2023.3262882 (March 2023). 

2. Chaddad A., lu Q., Li J, Katib Y., Kateb R,, Tanougast C., Bouridane A., Abdulkadir A., 2022 “Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine”, IEEE/CAA Journal of Automatica Sinica., vol.10, no.4, DOI: 10.1109/JAS.2023.123123 (March 2023).

3. Chaddad A., Hassan L., Desrosiers, C., 2022 “Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images”, IEEE Transactions on Neural Networks and Learning Systems., vol. 32, DOI: 10.1109/TNNLS.2021.3119071. 

4.  Chaddad A., Daniel P., Zhang M., Rathore S., Sargos P., Desrosiers, C., Niazi T., 2022 “Deep radiomic signature with immune cell markers predicts the survival of glioma patients”, Neurocomputing, https://doi.org/10.1016/j.neucom.2020.10.117. 

5.  Chaddad A., Sargos P., Desrosiers, C., 2021 “Modeling texture in deep 3D CNN for survival analysis”, IEEE Journal of Biomedical and Health Informatics, DOI: 10.1109/JBHI.2020.3025901. 

6.  Chaddad, A., Daniel, P., 2019. Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time. IEEE Journal of Biomedical and Health Informatics 23, 795–804. https://doi.org/10.1109/JBHI.2018.2825027. 

7. Chaddad, A., Hassan L., Katib Y., and Bouridane A, 2023. Deep survival analysis with clinical variables for COVID-19. IEEE Journal of Translational Engineering in Health and Medicine 2023,  DOI:10.1109/JTEHM.2023.3256966

8.  Chaddad, A., Peng, J., Xu, J. and Bouridane, A., 2023. Survey of Explainable AI Techniques in Healthcare. Sensors 2023, 23(2), 634; https://doi.org/10.3390/s23020634

9. Longzhao Huang; Yujie Li; Xu Wang; Haoyu Wang; Ahmed Bouridane; Ahmad Chaddad#, 2022 “Gaze estimation approach using deep differential residual network”, Sensors 2022, 22(14), 5462; https://doi.org/10.3390/s22145462

10. Chaddad A., Hassan L., Desrosiers, C., et al., 2021 “Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review”, Diagnostics 2021, 11, 2032. https://doi.org/10.3390/diagnostics11112032 

11.  Zhang, M., Zhang, M., Zhang, F., Chaddad A., and Evans, A., 2022 “Robust brain MR image compressive sensing via re-weighted total variation and sparse regression”, Magnetic Resonance Imaging. 85, 271-286, https://doi.org/10.1016/j.mri.2021.10.031

12.  Giraud N, Benziane N, Schick U, Beauval J-B, Chaddad A., Niazi T, Faye MD, Supiot S, Sargos P and Latorzeff I., 2021 “Post-Operative Radiotherapy in Prostate Cancer: Is It Time for a Belt and Braces Approach?” Front. Oncol. 11:781040. doi: 10.3389/fonc.2021.781040

13.  Chaddad A., Hassan L., Desrosiers, C., et al., 2021 “Deep CNN models for predicting COVID-19 in CT and x-ray images”, SPIE Journal of medical imaging, 8(S1), 1-13, https://doi.org/10.1117/1.JMI.8.S1.014502.

14.  Rathore S., Chaddad A., Iftikhar M., Bilello M., Abdulkadir A., 2021 “Combining MR and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma”, Radiology: Imaging Cancer, 2021; 3(4):e200108, https://doi.org/10.1148/rycan.2021200108.

15. Chaddad A., Hassan L., Katib Y., “Future Artificial Intelligence tools and perspectives in medicine”, Current Opinion in Urology, Volume 31 - Issue 4 - p 371-377, doi: 10.1097/MOU.0000000000000884.

16.  Chaddad A., Kucharczyk M., Cheddad A., EClarke S., Hassan L., Ding S., Rathore S., Zhang M., Katib Y., Bahoric B., Abikhzer G., Probst S., Niazi T., 2020 “ Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review”, Cancers 2021, 13(3), 552; https://doi.org/10.3390/cancers13030552.

17. Chaddad A., Kucharczyk MJ, Desrosiers, C., et al., 2020 “Deep radiomic analysis to predict Gleason Score in Prostate Cancer”, IEEE Access, DOI: 10.1109/ACCESS.2020.3023902

18. Ji, L., Zhang, R., Han, H., Chaddad, A., 2020 “Image Magnification Based on Bicubic Approximation with Edge as Constraint”, Appl. Sci., 10, 1865. https://doi.org/10.3390/app10051865

19. Saima Rathore *, Tamim Niazi, Aksam Iftikhar, Ashish Singh, Batool Rathore, Michel Bilello, Chaddad, A., 2020 “Multi-modal ensemble-based segmentation of white matter lesions and analysis of their differential characteristics across major brain regions”, Appl. Sci. 2020, 10(6), 1903; https://doi.org/10.3390/app10061903

20.  Rathore S., Niazi T., Iftikhar A., Chaddad, A., 2020 “Glioma grading via analysis of digital pathology images using machine learning”, Cancers 2020, 12, 578. https://doi.org/10.3390/cancers12030578

21. Kucharczyk MJ, Tsui JMG, Khosrow-Khavar F, Bahoric B, Souhami L, Anidjar M, Probst S, Chaddad A, Sargos P and Niazi T (2020) Combined Long-Term Androgen Deprivation and Pelvic Radiotherapy in the Post-operative Management of Pathologically Defined High-Risk Prostate Cancer Patients: Results of the Prospective Phase II McGill 0913 Study. Front. Oncol. 10:312. doi: 10.3389/fonc.2020.00312

22. Rathore S., Iftikhar A., Chaddad, A., et al., “Segmentation and grade prediction of colon cancer digital pathology images across multiple institutions”. Cancers, 2019, 11, 1700. https://doi.org/10.3390/cancers11111700

23. Chaddad, A., Daniel P., Sabri S., Desrosiers C., Abdulkarim B., 2019. “Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma”, Cancers, 2019, 11, 1148. https://doi.org/10.3390/cancers11081148. [Impact Factor=6.5]

24. Chaddad, A., Toews, M., Desrosiers C., Niazi, T., 2019. “Deep Radiomic analysis Based on Modeling Information flow in Convolutional Neural Networks”, IEEE Access, 10.1109/ACCESS.2019.2930238

25. Chaddad, A., Desrosiers, C., Abdulkarim, B., Niazi, T., 2019. “Multimodal radiomic features for predicting the gene status and survival outcome of lower-grade glioma patients”, IEEE Access, vol.7, 75976-75984, 10.1109/ACCESS.2019.2920396

26. Chaddad, A., Michael, J.K., Daniel, P., Sabri, S, Jean-Claude, B., Niazi, T., Abdulkarim, B., 2019. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Frontiers in oncology, DOI: 10.3389/fonc.2019.00374

27. Elakshar, S., James, M.G.T., Michael, J.K., Tomic, N., Fawaz, Z.S., Bahoric, B., Papayanatos, J., Chaddad, A., Niazi, T., 2019. Does interfraction cone beam computed tomography improve target localization in prostate bed radiotherapy? Technology in Cancer Research and Treatment 18. https://doi.org/10.1177/1533033819831962

28. Daniel, P., Sabri, S., Chaddad, A., Meehan, B., Jean-Claude, B., Rak, J., Abdulkarim, B.S., 2019. Temozolomide induced hypermutation in glioma: Evolutionary mechanisms and therapeutic opportunities. Frontiers in Oncology 9. https://doi.org/10.3389/fonc.2019.00041

29. Chaddad, A., Desrosiers, C., Niazi, T., 2018b. Deep radiomic analysis of MRI related to Alzheimer’s disease. IEEE Access 6, 58213–58221. https://doi.org/10.1109/ACCESS.2018.2871977

30. Chaddad, A., Niazi, T., Probst, S., Bladou, F., Anidjar, M., Bahoric, B., 2018d. Predicting gleason score of prostate cancer patients using radiomic analysis. Frontiers in Oncology 8. https://doi.org/10.3389/fonc.2018.00630

31. Chaddad, A., Kucharczyk, M.J., Niazi, T., 2018c. Multimodal radiomic features for the predicting gleason score of prostate cancer. Cancers 10. https://doi.org/10.3390/cancers10080249

32. Chaddad, A., Sabri, S., Niazi, T., Abdulkarim, B., 2018e. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Medical and Biological Engineering and Computing 56, 2287–2300. https://doi.org/10.1007/s11517-018-1858-4

33. Chaddad, A., Daniel, P., Niazi, T., 2018a. Radiomics evaluation of histological heterogeneity using multiscale textures derived from 3D wavelet transformation of multispectral images. Frontiers in Oncology 8. https://doi.org/10.3389/fonc.2018.00096

34. Chaddad, A., Desrosiers, C., Toews, M., 2017b. Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Scientific Reports 7. https://doi.org/10.1038/srep45639 

35. Chaddad, A., Desrosiers, C., Toews, M., Abdulkarim, B., 2017c. Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget 8, 104393–104407. https://doi.org/10.18632/oncotarget.22251

36. Chaddad, A., Desrosiers, C., Hassan, L., Tanougast, C., 2017a. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neuroscience 18. https://doi.org/10.1186/s12868-017-0373-0

37. Haj-Hassan, H., Chaddad, A.#, Harkouss, Y., Desrosiers, C., Toews, M., Tanougast, C., 2017. Classifications of multispectral colorectal cancer tissues using convolution neural network. Journal of Pathology Informatics 8. https://doi.org/10.4103/jpi.jpi_47_16

38. Chaddad, A., Tanougast, C., 2017. Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer. Analytical Cellular Pathology 2017. https://doi.org/10.1155/2017/8428102

39. Chaddad, A., Desrosiers, C., Hassan, L., Tanougast, C., 2016b. A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome. British Journal of Radiology 89. https://doi.org/10.1259/bjr.20160575

40. Chaddad, A., Tanougast, C., 2016b. Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients. Medical and Biological Engineering and Computing 54, 1707–1718. https://doi.org/10.1007/s11517-016-1461-5

41. Chaddad, A., Desrosiers, C., Bouridane, A., Toews, M., Hassan, L., Tanougast, C., 2016a. Multi texture analysis of colorectal cancer continuum using multispectral imagery. PLoS ONE 11. https://doi.org/10.1371/journal.pone.0149893

42. Chaddad, A., Tanougast, C., 2016a. Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Brain Informatics 3, 53–61. https://doi.org/10.1007/s40708-016-0033-7

43. Chaddad, A., Tanougast, C., 2015a. Real-time abnormal cell detection using a deformable snake model. Health and Technology 5, 179–187. https://doi.org/10.1007/s12553-015-0115-1

44. Chaddad, A., Tanougast, C., 2015b. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Advances in Bioinformatics 2015. https://doi.org/10.1155/2015/728164

45. Chaddad, A., 2015. Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models. International Journal of Biomedical Imaging 2015. https://doi.org/10.1155/2015/868031

46. Chaddad, A., 2014. Low-Noise Front-End Receiver Dedicated to Biomedical Devices: NIRS Acquisition System. Circuits and Systems 05, 191. https://doi.org/10.4236/cs.2014.58021

47. Chaddad, A., 2014. Brain Function Diagnosis Enhanced Using Denoised fNIRS Raw Signals. Journal of Biomedical Science and Engineering 07, 218. https://doi.org/10.4236/jbise.2014.74025

48. Chaddad, A., Tanougast, C., Golato, A., Dandache, A., 2013. Carcinoma cell identification via optical microscopy and shape feature analysis. Journal of Biomedical Science and Engineering 06, 1029. https://doi.org/10.4236/jbise.2013.611128

49. Chaddad, A., Tanougast, C., Dandache, A., Bouridane, A., 2011c. Extracted haralick’s texture features and morphological parameters from segmented multispectrale texture bio-images for classification of colon cancer cells. WSEAS Transactions on Biology and Biomedicine 8, 39–50.

Peer-reviewed article - conferences 

50. Chaddad A., Tanougast C., “Advances in MRI-Based Radiomics for Prostate Cancer”, IEEE ISBI 2023 (Accepted Jan. 22). 

51. Chaddad A., Tanougast C., “A One-Dimensional Convolutional Neural Network Model for Predicting the Survival Outcome of Coronavirus Disease 2019”, IEEE ISBI 2023 (Accepted Jan. 22). 

52.  Li Y., Tan B., Shuxue D., Desrosiers C., Chaddad A.#, “Symmetry Structured Analysis Sparse Coding for Key Frame Extraction”, ML4CS 2022, LNCS 13655, pp. 1–18, 2022. https://doi.org/10.1007/978-3-031-20096-0_44

53. Chaddad A., Zhang M., Hassan L., Niazi T., “Modeling of textures to predict immune cell status and survival of brain tumour patients”, 2021 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1067-1071, 2021,https://ieeexplore.ieee.org/document/9434053.

54. Zhang M., Zhang F., Zhang J., Chaddad A., Guo F., Zhang W., Zhang Ji., Evans A., “AutoEncoder for Neuroimage”, 32st International Conference on Database and Expert Systems Applications,2021, vol 12924,  https://doi.org/10.1007/978-3-030-86475-0_9

55. Zhang M., Zhao Z., Zhang W., Chaddad A., Evans A., Poline J.B., “Deep Discriminative Learning for Predicting Autism Spectrum Disorder”, 31st International Conference on Database and Expert Systems Applications, Pages 435-443, 2020, https://doi.org/10.1007/978-3-030-59003-1_29.

56. Chaddad, A., Zhang, M., Desrosiers, C., and Niazi, T., “Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma” MICCAI RNO-AI 2019. Pages 36-43, https://rd.springer.com/chapter/10.1007/978-3-030-40124-5_4

57. Rathore, S., Chaddad, A., et al., “Imaging signature of 1p/19q co-deletion status derived via machine learning in low-grade glioma” MICCAI RNO-AI 2019. Pages 61-69, https://www.springer.com/gp/book/9783030401238

58. Chaddad, A., Niazi, T., 2018. Radiomics analysis of subcortical brain regions related to Alzheimer disease, in: 2018 IEEE Life Sciences Conference, LSC 2018. pp. 203–206. https://doi.org/10.1109/LSC.2018.8572264

59. Kumar, K., Desrosiers, C., Chaddad, A., Toews, M., 2017. Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers, in: Proceedings - International Conference on Pattern Recognition. pp. 2162–2167. https://doi.org/10.1109/ICPR.2016.7899956

60.  Chaddad, A., Desrosiers, C., Toews, M., 2016d. Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme, in: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. pp. 4035–4038. https://doi.org/10.1109/EMBC.2016.7591612

61. Chaddad, A., Desrosiers, C., Hassan, L., Toews, M., 2016c. Multispectral texture analysis of histopathological abnormalities in colorectal tissues, in: Proceedings - International Conference on Image Processing, ICIP. pp. 2628–2632. https://doi.org/10.1109/ICIP.2016.7532835

62. Chaddad, A., Desrosiers, C., Toews, M., 2016f. Local discriminative characterization of MRI for Alzheimer’s disease, in: Proceedings - International Symposium on Biomedical Imaging. pp. 1–5. https://doi.org/10.1109/ISBI.2016.7493197

63. Chaddad, A., Desrosiers, C., Toews, M., 2016g. GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes, in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2214491

64. Chaddad, A., Desrosiers, C., Toews, M., 2016e. Phenotypic characterization of glioblastoma identified through shape descriptors, in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2209121

65. Chaddad, A., Bouridane, A., Hassan, L., Tanougast, C., 2015a. Wavelet based radiomics for brain tumour phenotypes discrimination, in: Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering. pp. 1167-1174. ISBN 978-1-5108-1745-6.

66. Chaddad, A., Bouridane, A., Tanougast, C., 2015b. Continuum analysis of colorectal cancer using texture feature extraction, in: Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering. pp. 1423-1430. ISBN 978-1-5108-1745-6.

67. Haj-Hassan, H., Chaddad, A., Tanougast, C., Harkouss, Y., 2015a. Hybrid segmentation of bio-images, in: Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering. pp. 1431-1437. ISBN 978-1-5108-1745-6.

68. Chaddad, A., Zinn, P.O., Colen, R.R., 2015c. Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM, in: Proceedings - International Symposium on Biomedical Imaging. pp. 84–87. https://doi.org/10.1109/ISBI.2015.7163822

69. Haj-Hassan, H., Chaddad, A., Tanougast, C., Harkouss, Y., 2015b. Comparison of segmentation techniques for histopathological images, in: 2015 5th International Conference on Digital Information and Communication Technology and Its Applications, DICTAP 2015. pp. 80–85. https://doi.org/10.1109/DICTAP.2015.7113175

70. Chaddad, A., Tanougast, C., 2014. Low-noise transimpedance amplifier dedicated to biomedical devices: Near infrared spectroscopy system, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 601–604. https://doi.org/10.1109/CoDIT.2014.6996963

71. Chaddad, A., Zinn, P.O., Colen, R.R., 2014c. Quantitative texture analysis for Glioblastoma phenotypes discrimination, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 605–608. https://doi.org/10.1109/CoDIT.2014.6996964

72. Haj-Hassan, H., Chaddad, A., Tanougast, C., Harkouss, Y., 2014. Segmentation of abnormal cells by using level set model, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 770–773. https://doi.org/10.1109/CoDIT.2014.6996994

73. Wangaryattawanich, P., Wang, J., Thomas, G.A., Chaddad, A., Zinn, P.O., Colen, R.R., 2014. Survival analysis of pre-operative GBM patients by using quantitative image features, in: Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014. pp. 625–627. https://doi.org/10.1109/CoDIT.2014.6996968

74. Chaddad, A., Colen, R.R., 2014. Statistical feature selection for enhanced detection of brain tumor, in: Proceedings of SPIE - The International Society for Optical Engineering. https://doi.org/10.1117/12.2062143

75. Chaddad, A., Zinn, P.O., Colen, R.R., 2014d. Brain tumor identification using Gaussian Mixture Model features and Decision Trees classifier, in: 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014. https://doi.org/10.1109/CISS.2014.6814077

76. Chaddad, A., 2014. Brain function evaluation using enhanced fNIRS signals extraction, in: 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014. https://doi.org/10.1109/CISS.2014.6814079

77. Chaddad, A., Ahmad, F., Amin, M.G., Sevigny, P., Difilippo, D., 2014a. Textural feature selection for enhanced detection of stationary humans in through-The-wall radar imagery, in: Proceedings of SPIE - The International Society for Optical Engineering. https://doi.org/10.1117/12.2049416

78. Chaddad, A., Tanougast, C., Dandache, A., 2014b. Snake method enhanced using canny approach implementation for cancer cells detection in real time, in: BIODEVICES 2014 - 7th Int. Conference on Biomedical Electronics and Devices, Proceedings; Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. pp. 187–192.

79. Boutalbi, M., Frihi, M., Toumi, S., Tanougast, C., Killian, C., Chaddad, A., Dandache, A., 2013. Reliable router for accurate online error detection in dynamic Network on Chip, in: 2013 25th International Conference on Microelectronics, ICM 2013. https://doi.org/10.1109/ICM.2013.6735021

80. Chaddad, A., Maamoun, M., Tanougast, C., Dandache, A., 2013b. Hardware implementation of active contour algorithm for fast cancer cells detection, in: Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013. pp. 129–130. https://doi.org/10.1109/SBEC.2013.73

81. Chaddad, A., Kamrani, E., Le Lan, J., Sawan, M., 2013a. Denoising fNIRS signals to enhance brain imaging diagnosis, in: Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013. pp. 33–34. https://doi.org/10.1109/SBEC.2013.25

82. Kamrani, E., Chaddad, A., Lesage, F., Sawan, M., 2013. Integrated transimpedance amplifiers dedicated to low-noise and low-power biomedical applications, in: Proceedings - 29th Southern Biomedical Engineering Conference, SBEC 2013. pp. 5–6. https://doi.org/10.1109/SBEC.2013.11

83. Chaddad, A., Tanougast, C., Dandache, A., Bouridane, A., 2011b. Extraction of Haralick features from segmented texture multispectral bio-images for detection of colon cancer cells, in: Proceedings - 1st International Conference on Informatics and Computational Intelligence, ICI 2011. pp. 55–59. https://doi.org/10.1109/ICI.2011.20

84. Chaddad, A., Tanougast, C., Dandache, A., Al Houseini, A., Bouridane, A., 2011a. Improving of colon cancer cells detection based on Haralick’s features on segmented histopathological images, in: ICCAIE 2011 - 2011 IEEE Conference on Computer Applications and Industrial Electronics. pp. 87–90. https://doi.org/10.1109/ICCAIE.2011.6162110

85. Chaddad, A., Tanougast, C., Dandache, A., Bouridane, A., 2011d. Classification of cancer cells based on morphological features from segmented multispectral bio-images, in: Recent Advances in Applied and Biomedical Informatics and Computational Engineering in Systems Applications - AIC’11, BEBI’11. pp. 92–97. Publisher Site

Peer-reviewed -abstract

1. Katib, Y.Y., Royal-Preyra, B., Sasson, T., Chaddad A., Sargos, P., Bahoric, B. and Niazi, T.M., 2020. Hematological Changes Associated With Prostate Radiotherapy And Androgen Deprivation Therapy. International Journal of Radiation Oncology, Biology, Physics, 108(3), p.e900. DOI:https://doi.org/10.1016/j.ijrobp.2020.07.516

2. Leduc N, Giraud N, Gandaglia G, Mathieu R, Ploussard G, Niazi T, Chaddad A., Vinh-Hung V, Sargos P, Beauval J-B, “Predicting biochemical recurrence after prostatectomy: can Machine Learning beat CAPRA score? Results of a multicentric retrospective analysis on 4700 patients”.  https://ascopubs.org/doi/abs/10.1200/JCO.2020.38.6_suppl.343

3. Daniel P, Meehan B, Sabri S, Chaddad, A, et al. “Exploiting Molecular Subtype Cell Plasticity as Novel Strategy for Targeting Glioma Stem Cells Through Alternating Therapy”. https://www.redjournal.org/article/S0360-3016(18)32197-7/fulltext

4. Chaddad, A., Radiomic analysis of GBM patients: a preliminary study for predicting overall survival. First International Summit in Radiation Oncology. 2016.

5. Zinn PO, Luedi MM, Singh SK, Gumin J, Chaddad, A., Hatami M, Shojaee Bakhtiari A, Lang FF, Colen RR. Functional validation of radiogenomics with a pre-clinical orthotopic glioblastoma model. Congress of Neurological Surgeons 2015 Annual Meeting Proceedings (#17275), 9/2015.

6. Zinn PO, Chaddad, A., Colen RR. Texture based computational models of glioblastoma phenotypes in radiological images. Congress of Neurological Surgeons 2015 Annual Meeting Proceedings (#17451), 9/2015.

7. Zinn P, Luedi M, Singh S, Chaddad, A., Bakhtiari A, Sulman E, Lang F, Colen R. Targeting radiogenomics-derived core periostin correlated gene networks in glioblastoma - a novel treatment approach. American Association of Neurological Surgeons 83rd Annual Scientific Meeting Proceedings, 5/2015.

8. Colen R, Luedi M, Singh S, Chaddad, A., Bakhtiari A, Zinn P. Identification of gene specific MRI texture features in first radiogenomic model. American Society of Neuroradiology 53rd Annual Meeting Proceedings (#O-430), 4/2015.

9. Colen R, Bakhtiari A, Chaddad, A., Luedi, Zinn P. Radiomic subclassification of glioblastoma. American Society of Neuroradiology 53rd Annual Meeting Proceedings (#O-435), 4/2015.

10. Chaddad, A., Luedi M, Zinn P, Colen R. Texture analysis for assessing of Glioblastoma heterogeneity. American Society of Neuroradiology 53rd Annual Meeting Proceedings (#1589), 4/2015.

11. Chaddad, A., Zinn PO, Colen RR. Texture feature selection for enhanced assessing of glioblastoma heterogeneous. GAP 2015 Conference Proceedings, 4/2015.

12. Chaddad, A., Zinn PO, Colen RR. Extraction of phenotype texture features by waveletbased method in glioblastoma. American Society of Functional Neuroradiology 9th Annual Meeting Proceedings, 3/2015.

13. Chaddad, A., Zinn P, Colen RR. Wavelet based feature approach for radiomic texture extraction from glioblastoma phenotypes. J Nucl Med 56(2 (Suppl)):3, 2/2015.

14. Chaddad, A., Zinn PO, Colen RR. Abnormal cells discrimination using the different shape parameters. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#EP-94), 5/2014.

15. Chaddad, A., Zinn PO, Colen RR. Brain tumor identification using gaussian mixture model features and decision trees classifier. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#O-829), 5/2014. 

16. Chaddad, A., Colen RR, Zinn PO. Carcinoma cells type identification based on the texture analysis. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#EP-228), 5/2014. 

17. Colen RR, Chaddad, A., Zinn PO. Integrated imageomic analysis identifies clinically relevant imaging subtypes of glioblastoma. American Society of Neuroradiology 52nd Annual Meeting Proceedings (#O-371), 5/2014.

18. Ashour OZ, Chaddad, A., Zinn PO, Colen RR. Introduction to segmentation, registration and volume analysis for imaging genomics. American

Course development and lecturing

• 2021-2022: Digital signal processing

• 2021-2022: Digital system design

• 2009-2018: Electronic circuits

• 2016-2018: Computer vision

• 2017-2018: Biometric system