Artificial Intelligence
against cancer


Artificial Intelligence for medical image analysis

Knowing that the earlier cancer is detected, the better the life expectancy, accurate radiological detection can make the difference between life and death. Even the most experienced radiologist can make mistakes, and in up to 70% of cases, undetected cancer was visible retrospectively on previous imaging examinations. Unfortunately, because of the current shortage of radiologists in the healthcare system, access to second opinion is often not possible.

Existing Computed Assisted Diagnosis (CADx) software use traditional image analysis techniques and even with their accuracy in dispute, any performance improvement is better than none in this second viewer approach. However the high rate of false positives and false negative has refrained most of radiologists to adopt them in their daily practices. Artificial intelligence with its recent deep learning techniques is already providing superhuman pattern recognition performance and is anticipated to become a game changer in medical image analysis.



Our values are oriented toward a single goal: help the fight against cancer which will affect almost half of us. Everyone should be able to receive an accurate and timely diagnosis, the essential first condition for effective treatment and care.



By using a data set of tumor data labelled at the per-pixel level for each modality and each type of cancer, we aim to provide a DCNN model than can detect cancer at an expert level.



By combining back-propagation learning with higher-order graphical models, our software will produce segmentation of various modalities.



By developing longitudinal registration algorithms between multiple scans, our software will allow follow-up examinations of tumor progression.



Classification for stratifying tumors, based on tumor segmentations will allow radiologist to perform a faster diagnosis.




We show empathy. Understanding the difficulty to cope with cancer or the cancer of a loved one is the first step to motivate our team in making cancer a thing of the past.


We inspire creativity. Innovative approaches are difficult since they require change. But only change brings opportunity. Traditional methods have worked only half-way. We bridge the gap by providing the resources to think.


We work together. Inter disciplinarily is key to success in winning the battle against cancer. Combining the best minds in Machine Learning, Radiology, Oncology, Artificial Intelligence, Optimization, and Biology is the best way to have a working solution for the ultimate benefit of the patient.


We thrive only in excellence. Life is too precious to be careless. We are the machine learning and optimization center of excellence in oncological imaging. We are passionate, and passion demands perseverance: improvements becomes possible by shooting for the impossible and giving it our best try. And again. We succeed where others have failed because we try one more time.


We believe in education. That sciences such as artificial intelligence and operations research can bring game-changing decision support systems to medical oncology.


Alexandre Le Bouthillier, Ph.D.


Ph.D. Parallel Optimization, Université de Montréal; Post-doc Université de Genève

Co-founder of Planora (aquired by RedPrairie)

VP Science & Technology at RedPrairie

20 years in leading large storage and server projects for FDA certification, hospitals, space agency and cities






Nicolas Chapados, Ph.D., CFA

Chief Science Officer

Ph.D. in machine learning, Université de Montréal

Co-founder of ApSTAT Technologies (machine learning) and of Chapados Couture Capital

Adjunct professor of applied mathematics at École Polytechnique de Montréal

20 years experience in machine learning






Frédéric Francis, B.Sc.

Chief Operating Officer

BSc. Physics from McGill University

Co-Founder & CEO of Resonant Medical, leader in 3D ultrasound image-guidance for prostate & breast cancer radiotherapy

CEO of Harmonic Medical, developer of next-generation focused ultrasound therapy

Co-Founder of Lateral Logic, pioneer in real-time rigid-body dynamics for visual simulation & virtual prototyping

15 years experience in medical imaging and image-guidance oncology applications





Geralyn Ochab

VP Sales & Business Dev.

Executive Business Manager, Toshiba

VP Sales & Marketing, Insception, Canada’s largest cord blood stem cell bank

National Sales & Marketing Manager, Emerging Ultrasound, GE Healthcare

Director Clinical Business Dev, Resonant Medical, 3D U/S radiotherapy

Director Business Dev. Medipattern, 3D breast ultrasound CAD solutions

General Manager, Life Imaging Systems, 3D medical imaging software

20 years medical imaging industry, 10 years clinical ultrasound & vascular




Yoshua Bengio, Ph.D.

Scientific Advisor (Machine Learning)

Ph.D. in Computer Science, McGill University; Postdocs at MIT and AT&T Bell Labs

Full professor, department of computer science and operations research, Université de Montréal

Canada Research Chair in statistical learning algorithms, CIFAR Fellow, NSERC-Ubisoft industrial chair

One of the world’s foremost researchers in machine learning, and a leading figure in « deep learning »



We are hiring

If you want to be part of a world-class team of exceptional talents fighting cancer please get in touch.

We are looking for deep learning interns, fellows and researchers.

Please send your CV at


803-4200 blvd. Saint-Laurent
Montreal, Qc, H2W 2R2
Toll free: 1(855)7IMAGIA
Tel/Fax: +1(438)800-0487