Gabriel Maicas Suso

Australian Institute for Machine Learning

Division of Research and Innovation

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

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.

My Research
Career
Publications
Grants and Funding
Supervision
Contact

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.

Appointments

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

Awards and Achievements

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	-

Education

Date Institution name Country Title

2018 Australian Institute for Machine Learning Australia PhD

2014 Universidad Rey Juan Carlos Spain MsC
Research Interests

Biomedical Engineering Computer Vision Knowledge Representation and Machine Learning

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

Journals

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 Scopus23 WoS13 Europe PMC14
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 Scopus18 WoS6 Europe PMC5
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 Scopus37 WoS27 Europe PMC14
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 Scopus20 WoS12
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 Scopus23 WoS16 Europe PMC4
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.

Book Chapters

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 Scopus1
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

Conference Papers

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 Vol. 27 (pp. 15 pages). Online: ACM. DOI Scopus8
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 Scopus2
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 Scopus3
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 Scopus7 WoS4
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 Scopus9
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 Scopus16
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 WoS5
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 Scopus5 WoS2
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 Scopus18
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 WoS3
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 Scopus14 WoS9
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 Scopus19 WoS12
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 Scopus47 WoS29
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) Vol. 34 (pp. 305-309). Online: IEEE. DOI Scopus27 WoS21
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 Scopus84

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

Past Higher Degree by Research Supervision (University of Adelaide)

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

Email: gabriel.maicassuso@adelaide.edu.au

Gabriel Maicas Suso

Appointments

Awards and Achievements

Education

Research Interests

Journals

Book Chapters

Conference Papers

Past Higher Degree by Research Supervision (University of Adelaide)

Connect With Me

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