Gabriel Maicas Suso

Research Interests

Biomedical Engineering Computer Vision Knowledge Representation and Machine Learning

Gabriel Maicas Suso

Office of Engineering and Information Technology

College of Engineering and Information Technology

Gabriel Maicas is the AI Lead for the Women’s and Children’s Hospital (WCH) at The Australian Institute for Machine Learning (AIML). Gabriel leads the development and integration of state-of-the-art machine learning research into WCH to improve patient outcomes and experience, and hospital efficiency. Gabriel’s research focuses on a range of health fields where AI has the potential to benefit the broader society and have a greater impact on patients.Previously, Gabriel was a research assistant at AIML focused on several health-AI areas including the early diagnosis of diseases and personalised medicine to improve treatment decisions. Gabriel obtained his PhD in Medical Image Analysis from The University of Adelaide (Australia) in 2018. Gabriel received his Master’s degree in Computer Vision from Universidad Rey Juan Carlos (Madrid, Spain) and graduated from Universidad Autonoma de Madrid (Madrid, Spain) with a double major in Mathematics and Computer Science.

Gabriel’s research focuses on a range of health fields where AI has the potential to benefit the broader society and have a greater impact on patients.

Date Position Institution name
2021 - ongoing AI Lead for Women's and Children's Hospital Australian Institute for Machine Learning
2019 - 2021 Research Fellow Australian Institute for Machine Learning

Date Type Title Institution Name Country Amount
2018 Award Dean’s Commendation for Doctoral Thesis Excellence The University of Adelaide Australia -
2018 Award MICCAI Student Travel Award MICCAI United States -

Date Institution name Country Title
2018 Australian Institute for Machine Learning Australia PhD
2014 Universidad Rey Juan Carlos Spain MsC

Year Citation
2021 Bedrikovetski, S., Dudi-Venkata, N. N., Maicas Suso, G., Kroon, H. M., Seow, W., Carneiro, G., . . . Sammour, T. (2021). Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: a systematic review and meta-analysis. Artificial Intelligence in Medicine, 113, 1-11.
DOI Scopus31 WoS28 Europe PMC25
2021 Maicas Suso, G., Leonardi, M., Avery, J., Panuccio, C., Carneiro, G., Hull, M. L., & Condous, G. (2021). Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign. Reproduction and Fertility, 2(4), 236-243.
DOI Scopus30 WoS25 Europe PMC14
2020 Cheng Tao Pu, L. Z., Maicas, G., Tian, Y., Yamamura, T., Nakamura, M., Suzuki, H., . . . Singh, R. (2020). Computer-aided diagnosis for characterisation of colorectal lesions: a comprehensive software including serrated lesions. Gastrointestinal Endoscopy, 92(4), 891-899.
DOI Scopus42 WoS38 Europe PMC23
2020 Jonmohamadi, Y., Takeda, Y., Liu, F., Sasazawa, F., Maicas, G., Crawford, R., . . . Carneiro, G. (2020). Automatic segmentation of multiple structures in knee arthroscopy using deep learning. IEEE Access, 8, 51853-51861.
DOI Scopus30 WoS19
2019 Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. Medical Image Analysis, 58, 101562-1-101562-14.
DOI Scopus30 WoS24 Europe PMC14
2019 Liu, Y., Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2019). Photoshopping Colonoscopy Video Frames.. CoRR, abs/1910.10345.
2019 Glaser, S., Maicas, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2019). Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT.. CoRR, abs/1910.10371.

Year Citation
2019 Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI. In L. Lu, X. Wang, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics (pp. 163-178). Cham, Switzerland: Springer.
DOI Scopus2
2016 Maicas Suso, G., Muñoz, A. I., Galiano, G., Ben Hamza, A., & Schiavi, E. (2016). Spectral shape analysis of the hippocampal structure for Alzheimer’s disease diagnosis. In F. Ortegon Gallego, M. Redondo Neble, & J. Rodriguez Galvan (Eds.), Trends in Differential Equations and Applications (Vol. 8, pp. 17-32). Switzerland: Springer International Publishing.
DOI Scopus2

Year Citation
2023 Mccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., . . . Goldenberg, A. (2023). What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning. In 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 15 pages). Online: ACM.
DOI Scopus17 WoS17
2022 Hermoza, R., Maicas, G., Nascimento, J. C., & Carneiro, G. (2022). Censor-Aware Semi-supervised Learning for Survival Time Prediction from Medical Images. In Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022), as published in Lecture Notes in Computer Science Vol. 13437 (pp. 213-222). Singapore: Springer.
DOI Scopus4 WoS3
2022 Snaauw, G., Sasdelli, M., Maicas, G., Lau, S., Verjans, J., Jenkinson, M., & Carneiro, G. (2022). Mutual information neural estimation for unsupervised multi-modal registration of brain images. In 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2022-July (pp. 3510-3513). Online: IEEE.
DOI Scopus8 WoS7
2021 Hermoza Aragones, R., Maicas Suso, G., Nascimento, J. C., & Carneiro, G. (2021). Post-hoc overall survival time prediction from brain MRI. In Proceedings of the IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021) Vol. 2021-April (pp. 1476-1480). online: IEEE.
DOI Scopus9 WoS8
2020 Liu, F., Jonmohamadi, Y., Maicas Suso, G., Pandey, A. K., & Carneiro, G. (2020). Self-supervised depth estimation to regularise semantic segmentation in knee arthroscopy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12261 LNCS (pp. 594-603). Switzerland: Springer Nature.
DOI Scopus10
2020 Hermoza Aragones, R., Maicas Suso, G., Nascimento, J. C., & Carneiro, G. (2020). Region proposals for saliency map refinement for weakly-supervised disease localisation and classification. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 23rd International Conference, Lima, Peru, October 4–8, 2020. Proceedings, Part VI Vol. 12266 LNCS (pp. 539-549). Switzerland: Springer Nature.
DOI Scopus22
2020 Liu, Y., Tian, Y., Maicas Suso, G., Zorron Cheng Tao Pu, L., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Photoshopping colonoscopy video frames. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1-5). Iowa City, Iowa, USA: IEEE.
DOI Scopus5 WoS11
2020 Glaser, S., Maicas Suso, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2020). Semi-supervised multi-domain multi-task training for metastatic colon lymph node diagnosis from abdominal CT. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1478-1481). Iowa City, Iowa, USA: IEEE.
DOI Scopus8 WoS5
2020 Tian, Y., Maicas Suso, G., Zorron Cheng Tao Pu, L., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Few-shot anomaly detection for polyp frames from colonoscopy. In Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2020), as published in Lecture Notes in Computer Science Vol. 12266 (pp. 274-284). Switzerland: Springer Nature.
DOI Scopus24
2020 Maicas, G., Nguyen, C., Motlagh, F., Nascimento, J. C., & Carneiro, G. (2020). Unsupervised task design to meta-train medical image classifiers. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1339-1342). Iowa City, Iowa, USA: IEEE.
DOI Scopus7 WoS4
2019 Maicas Suso, G., Snaauw, G., Bradley, A. P., Reid, I., & Carneiro, G. (2019). Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI. In Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Vol. 2019-April (pp. 1057-1060). online: IEEE.
DOI Scopus15 WoS12
2019 Snaauw, G., Gong, D., Maicas, G., van den Hengel, A., Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): Proceedings Vol. 2019-April (pp. 802-805). online: IEEE.
DOI Scopus25 WoS15
2018 Maicas Suso, G., Bradley, A., Nascimento, J., Reid, I., & Carneiro, G. (2018). Training medical image analysis systems like radiologists. In A. Frangi, J. Schnabel, C. Davatzikos, C. Alberola-Lopez, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018: proceedings, part 1 Vol. 11070 LNCS (pp. 546-554). Granada: Springer.
DOI Scopus54 WoS37
2017 Maicas, G., Carneiro, G., & Bradley, A. (2017). Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 305-309). Online: IEEE.
DOI Scopus28 WoS25
2017 Maicas, G., Carneiro, G., Bradley, A., Nascimento, J., & Reid, I. (2017). Deep reinforcement learning for active breast lesion detection from DCE-MRI. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, & S. Duchesne (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2017: 20th International Conference. Proceedings, Part III Vol. 10435 LNCS (pp. 665-673). Quebec City, Canada: Springer.
DOI Scopus97

2021 - Center for Augmented Reasoning - Funding for 1 PhD

2020 - Endometriosis Australia (Co-Applicant) - $ 30,000

2019 - eHealth Innovation Royal Adelaide Hospital (Co-Applicant) -  59,900

2019 - Australian Gynaecologic Endoscopy Society (Co-Applicant) - $ 9,570

Date Role Research Topic Program Degree Type Student Load Student Name
2019 - 2022 Co-Supervisor Weakly Supervised Localisation for Censor Aware Survival Prediction from Medical Images Doctor of Philosophy Doctorate Full Time Mr Renato Hermoza Aragones

Connect With Me

External Profiles

Other Links