Intelligence artificielle appliquée à l’imagerie médicale et à la thérapie personnalisée

Le développement de l’intelligence artificielle dans le domaine de la médecine est en effervescence, notamment dans les secteurs de la radiologie, de la radio-oncologie et de la médecine nucléaire.

Nous avons une masse critique d’experts en intelligence artificielle (IA) appliquée à l’imagerie médicale et à la thérapie personnalisée, parmi les professeurs et chercheurs du Département de radiologie, radio-oncologie et médecine nucléaire, et dans leurs réseaux.  Les activités communes de ces chercheurs ont mené jusqu’ici à de nombreuses publications (1-40) et l’obtention de plusieurs subventions.

Plusieurs membres de notre Département ont d’importantes activités en IA :  H Bahig, N Bureau, JF Carrier, G Cloutier, B Desjardins, A Diouf, L Létourneau-Guillon, O Monchi K Nelson, C Ménard, D Roberge, A Tang.  Nous avons également recruté récemment S Kadoury (Poly Montréal), A Cadrin-Chênevert (Université Laval), à titre de professeurs associés. Nous avons également des collaborateurs hors du Département:  M Chassé (Département de médecine), I Ben Ayed (École de technologie supérieure), A Lalonde (Département de physique).

Nouvelles connexes :

Intelligence artificielle – Octroi d’une subvention des IRSC au Dr Houda Bahig

AVC : un traitement plus rapide et personnalisé grâce à l’intelligence artificielle

Nos chercheurs dans les médias – L’intelligence artificielle contre le cancer

Récipiendaire Onco-Tech : Projet des Dr Samuel Kadoury, Dr David Roberge et leur équipe, Polytechnique Montréal, CHUM

Récipiendaire Onco-Tech : Le projet des Dr Guy Cloutier, Dr An Tang et leur équipe, Centre de recherche du CHUM

Octroi d’une bourse FRQS de carrière au Dre Houda Bahig ainsi qu’au Dr Mathieu Dehaes

Le Dr Maxime Bouthillier, résident en radiologie, reçoit une subvention de la Radiological Society of North America

Le titre de Fellow de l’Association canadienne des radiologistes est octroyé au Dr An Tang

Laurent Létourneau-Guillon, Prix Bernadette-Nogrady 2022 de la Société de radiologie du Québec

Publications choisies en intelligence artificielle des membres du Département de radiologie, radio-oncologie et médecine nucléaire (membres en caractères gras)

  1. Vorontsov E, Tang A, Roy D, Pal CJ, Kadoury C. Metastatic liver tumour segmentation with a neural network guided 3D deformable model. Medical & Biological Engineering & Computing (MBEC). 2017 Jan;55(1):127-139. doi: 10.1007/s11517-016-1495-8.
  2. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A. Deep Learning: A Primer for Radiologists. Radiographics. 2017 Nov-Dec;37(7):2113-2131.
  3. Drozdzal M, Chartrand G, Vorontsov E, Shakeri M, Di Jorio L, Tang A, Romero A, Bengio Y, Pal C, Kadoury S. Learning normalized inputs for iterative estimation in medical image segmentation. Med Image Anal. 2017 Nov 14;44:1-13.
  4. Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, Chepelev L, Cairns R, Mitchell JR, Cicero MD, Poudrette MG, Jaremko JL, Reinhold C, Gallix B, Gray B, Geis R; Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J. 2018 Apr 11.
  5. Vorontsov E, Cerny M, Régnier P, Di Jorio L, Pal C, Lapointe R, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep Learning for Automated Segmentation of Liver Lesions on Computed Tomography in Patients with Colorectal Cancer Liver Metastases. Radiology: Artificial Intelligence. 2019. https://pubs.rsna.org/doi/10.1148/ryai.2019180014
  6. Jaremko JL, Azar M, Bromwich R, Lum A, Alicia Cheong LH, Gibert M, Laviolette F, Gray B, Reinhold C, Cicero M, Chong J, Shaw J, Rybicki FJ, Hurrell C, Lee E, Tang A. Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology. Can Assoc Radiol J. 2019 Apr 5. pii: S0846-5371(19)30006-3. doi: 10.1016/j.carj.2019.03.001.
  7. Geis JR, Brady A, Wu CC, Spencer J, Ranschaert E, Jaremko JL, Langer SG, Kitts AB, Birch J, Shields WF, van den Hoven van Genderen R, Kotter E, Gichoya JW, Cook TS, Morgan MB, Tang A, Safdar NM, Kohli M. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology. 2019 Oct 1:191586.
  8. Bureau NJ, Destrempes F, Acid S, Lungu E, Moser T, Michaud J, Cloutier G. Diagnostic Accuracy of Echo Envelope Statistical Modeling Compared to B-Mode and Power Doppler Ultrasound Imaging in Patients With Clinically Diagnosed Lateral Epicondylosis of the Elbow. J Ultrasound Med. 2019;38:2631-2641.
  9. Maaref A, Perdigon Romero F, Montagnon E, Cerny M, Nguyen B, Vandenbroucke-Menu F, Geneviève S, Turcotte S, Tang A, Kadoury S. Predicting the Response to FOLFOX-based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: A Deep Neural Network Approach. Journal of Digital Imaging. 2020 Mar 19. doi: 10.1007/s10278-020-00332-2.
  10. Gan-Or Z, Rao T, Leveille E, Degroot C, Chouinard S, Cicchetti F, Dagher A, Das S, Desautels A, Drouin-Ouellet J, Durcan T, Gagnon JF, Genge A, Karamchandani J, Lafontaine AL, Sun SLW, Langlois M, Levesque M, Melmed C, Panisset M, Parent M, Poline JB, Postuma RB, Pourcher E, Rouleau GA, Sharp M, Monchi O, Dupré N, Fon EA. (2020) The Quebec Parkinson Network: A Researcher-Patient Matching Platform and Multimodal Biorepository. Journal of Parkinson’s Disease. 10(1):301-313.
  11. Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, Vandenbroucke-Menu F, Turcotte S, Kadoury S, Tang A. Deep Learning Workflow in Radiology: a Primer. Insights Imaging. 2020 Feb 10;11(1):22.
  12. Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R. Artificial. Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am. 2020;30:e1-e15.
  13. van Der Pol CB, Tang A. Imaging database preparation for machine learning. Can Assoc Radiol J. 2020 Oct 16:846537120967720.
  14. Destrempes F, Trop I, Allard L, Chayer B, Garcia-Duitama J, El Khoury M, Lalonde L, Cloutier G. Added Value of Quantitative Ultrasound and Machine Learning in BI-RADS 4-5 Assessment of Solid Breast Lesions. Ultrasound Med Biol. 2020 Feb;46(2):436-444
  15. Cheng P, Montagnon E, Yamashita R, Pan I, Cadrin-Chênevert A, Perdigon Romero F, Chartrand G, Kadoury S, Tang A. Deep learning: an update for radiologists. RadioGraphics. 2021;41(5):1427-45.
  16. Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, …, Alida A. Postma, Laurent Letourneau-Guillon, Gloria J. Guzman Perez-Carrillo, …, Adam E. Flanders, Spyridon Bakas (2021). The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. arXiv:2107.02314
  17. Ramezani M, Mouches P, Yoon E, Rajashekar D, Ruskey JA, Leveille E, Martens K, Kibreab M, Hammer T, Kathol I, Maarouf N, Sarna J, Martino D, Pfeffer G, Gan-Or Z, Forkert ND, Monchi O. Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning. Sci Rep. 2021 Mar 1;11(1):4917.
  18. Yu M, Tang A, Brown K, Bouchakri R, St-Onge P, Wu S, Reeder J, Mullie L, Chassé M. Integrating artificial intelligence in bedside care for covid-19 and future pandemics. BMJ. 2021 Dec 31;375:e068197.
  19. Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep. 2021;11:17633. Erratum in: Sci Rep. 2021 Sep 17;11(1):18908.
  20. Roy Cardinal MH, Durand M, Chartrand-Lefebvre C, Soulez G, Tremblay C, Cloutier G; for the Canadian HIV and Aging Cohort Study. Associative prediction of carotid artery plaques based on ultrasound strain imaging and cardiovascular risk factors in people living with HIV and age-matched control subjects of the CHACS cohort. Journal of Acquired Immune Deficiency Syndromes (JAIDS) (in press) 2022
  21. Gourdeau D, Potvin O, Archambault P, Chartrand-Lefebvre C, Dieumegarde L, Forghani R, Gagné C, Hains A, Hornstein D, Le H, Lemieux S, Lévesque MH, Martin D, Rosenbloom L, Tang A, Vecchio F, Yang I, Duchesne N, Duchesne S. Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning. Sci Rep. 2022 Apr 4;12(1):5616.
  22. Gourdeau D, Potvin O, Biem JH, Cloutier F, Abrougui L, Archambault P, Chartrand-Lefebvre C, Dieumegarde L, Gagné C, Gagnon L, Giguère R, Hains A, Le H, Lemieux S, Lévesque MH, Nepveu S, Rosenbloom L, Tang A, Yang I, Duchesne N, Duchesne S. Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients. Sci Rep. 2022 Apr 13;12(1):6193.
  23. Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR.Interpretation of radiomics features-A pictorial review. Comput Methods Programs Biomed. 2022 Mar;215:106609.
  24. Destrempes F, Gesnik M, Chayer B, Roy-Cardinal MH, Olivié D, Giard JM, Sebastiani G, Nguyen BN, Cloutier G, Tang A. Quantitative ultrasound, elastography and machine learning for assessment of steatosis, inflammation and fibrosis in chronic liver disease. PLOS One. 2022;17(1):e0262291.
  25. Le WT, Vorontsov E, Romero FP, Seddik L, Elsharief MM, Nguyen-Tan PF, Roberge D, Bahig H, Kadoury S. Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks. Sci Rep. 2022;12:3183.
  26. Mohamed El Amine Elforaici, Emmanuel Montagnon, Feryel Azzi, Dominique Trudel, Bich Nguyen, Simon Turcotte, An Tang, Samuel Kadoury. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022
  27. Li H, Bhatt M, Qu Z, Zhang S, Hartel MC, Khademhosseini A, Cloutier G. Deep learning in ultrasound elastography imaging: A review. Med Phys. 2022;49:5993-6018
  28. Vorontsov E, Molchanov P, Gazda M, Beckham C, Kautz J, Kadoury S. Towards annotation-efficient segmentation via image-to-image translation. Med Image Anal. 2022;82:102624.
  29. Saber R, Henault D, Messaoudi N, Rebolledo R, Montagnon E, Soucy G, Stagg J, Tang A, Turcotte S, Kadoury S. Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases. J Transl Med. 2023;21:507.
  30. Yamga E, Mullie L, Durand M, Cadrin-Chenevert A, Tang A, Montagnon E, Chartrand-Lefebvre C, Chassé M. Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis. Front Digit Health. 2023;5:1142822
  31. Tanguay W, Acar P, Fine B, Abdolell M, Gong B, Cadrin-Chênevert A, Chartrand-Lefebvre C, Chalaoui J, Gorgos A, Chin AS, Prénovault J, Guilbert F, Létourneau-Guillon L, Chong J, Tang A. Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework. Can Assoc Radiol J. 2023;74:326-333.
  32. Vazquez Romaguera L, Alley S, Carrier JF, Kadoury S. Conditional-Based Transformer Network With Learnable Queries for 4D Deformation Forecasting and Tracking. IEEE Trans Med Imaging. 2023;42:1603-1618.
  33. Vianna P, Calce SI, Boustros P, Larocque-Rigney C, Patry-Beaudoin L, Luo YH, Aslan E, Marinos J, Alamri TM, Vu KN, Murphy-Lavallée J, Billiard JS, Montagnon E, Li H, Kadoury S, Nguyen BN, Gauthier S, Therien B, Rish I, Belilovsky E, Wolf G, Chassé M, Cloutier G, Tang A. Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis. Radiology. 2023;309:e230659.
  34. Touati R, Kadoury S. A least square generative network based on invariant contrastive feature pair learning for multimodal MR image synthesis. Int J Comput Assist Radiol Surg. 2023;18:971-979.
  35. Mansouri M, Therasse E, Montagnon E, Zhan YO, Lessard S, Roy A, Boucher LM, Steinmetz O, Aslan E, Tang A, Chartrand-Lefebvre C, Soulez G. CT analysis of aortic calcifications to predict abdominal aortic aneurysm rupture. Eur Radiol. 2023. Epub ahead of print.
  36. Bang C, Bernard G, Le WT, Lalonde A, Kadoury S, Bahig H. Artificial intelligence to predict outcomes of head and neck radiotherapy. Clin Transl Radiat Oncol. 2023;39:100590.
  37. Touati R, Kadoury S. Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis. Phys Med Biol. 2023;68.
  38. Souza R, Wilms M, Camacho M, Pike GB, Camicioli R, Monchi O, Forkert ND. Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J Am Med Inform Assoc. 2023;30:1925-1933.
  39. Almgren H, Camacho M, Hanganu A, Kibreab M, Camicioli R, Ismail Z, Forkert ND, Monchi O. Machine learning-based prediction of longitudinal cognitive decline in early Parkinson’s disease using multimodal features. Sci Rep. 2023;13:13193.
  40. Camacho M, Wilms M, Mouches P, Almgren H, Souza R, Camicioli R, Ismail Z, Monchi O, Forkert ND. Explainable classification of Parkinson’s disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets. Neuroimage Clin. 2023;38:103405.