Max-Coupled Learning: Application to Breast Cancer. - Jaime S. Cardoso 001; Inês Domingues (ICMLA (1), 2011)

Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches. - Jaime S. Cardoso 001; Ricardo Gamelas Sousa; Inês Domingues (ICMLA (1), 2012)

A New Local Adaptive Mass Detection Algorithm in Mammograms. - Ehsan Koozegar; Mohsen Soryani; Inês Domingues (BIOSIGNALS, 2013)

Normal breast identification in screening mammography: A study on 18 000 images. - Sílvia Bessa; Inês Domingues; Jaime S. Cardoso 001; Pedro Passarinho; Pedro Cardoso; Vitor Rodrigues; Fernando Lage (BIBM, 2014)

Using Bayesian surprise to detect calcifications in mammogram images. - Inês Domingues; Jaime S. Cardoso 001 (EMBC, 2014)

Max-Ordinal Learning. - Inês Domingues; Jaime S. Cardoso 001 (IEEE Trans. Neural Networks Learn. Syst., 2014)

Closed Shortest Path in the Original Coordinates with an Application to Breast Cancer. - Jaime S. Cardoso 001; Inês Domingues; Hélder P. Oliveira (Int. J. Pattern Recognit. Artif. Intell., 2015)

An automatic mammogram system: from screening to diagnosis - Inês Domingues (2015)

A Personal Robot as an Improvement to the Customers' In-store Experience. - António J. R. Neves; Daniel Filipe Barros Campos; Fabio Duarte; Filipe Pereira; Inês Domingues; Joana Santos; João Leão; José Xavier; Luís M. O. de Matos; Manuel Camarneiro; Marcelo Penas; Maria Miranda; et al. (SMARTGREENS/VEHITS (Selected Papers), 2017)

Functionalities and Requirements of an Autonomous Shopping Vehicle for People with Reduced Mobility. - António J. R. Neves; Daniel Filipe Barros Campos; Fabio Duarte; Inês Domingues; Joana Santos; João Leão; José Xavier; Luís M. O. de Matos; Manuel Camarneiro; Marcelo Penas; Maria Miranda; Ricardo Silva; et al. (VEHITS, 2017)

Interpreting deep learning models for ordinal problems. - José Pereira Amorim; Inês Domingues; Pedro Henriques Abreu; João A. M. Santos (ESANN, 2018)

Bi-Rads Classification of Breast Cancer: A New Pre-Processing Pipeline for Deep Models Training. - Inês Domingues; Pedro H. Abreu; João A. M. Santos (ICIP, 2018)

Evaluation of Oversampling Data Balancing Techniques in the Context of Ordinal Classification. - Inês Domingues; José Pereira Amorim; Pedro H. Abreu; Hugo Duarte; João A. M. Santos (IJCNN, 2018)

Registration of CT with PET: A Comparison of Intensity-Based Approaches. - Gisèle Pereira; Inês Domingues; Pedro Martins 003; Pedro H. Abreu; Hugo Duarte; João A. M. Santos (IWCIA, 2018)

Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs. - Gabriel Silva; Inês Domingues; Hugo Duarte; João A. M. Santos (DICTA, 2019)

Going Back to Basics on Volumetric Segmentation of the Lungs in CT: A Fully Image Processing Based Technique. - Ana Catarina Oliveira; Inês Domingues; Hugo Duarte; João A. M. Santos; Pedro H. Abreu (IbPRIA (2), 2019)

An iterative oversampling approach for ordinal classification. - Francisco Marques; Hugo Duarte; João A. M. Santos; Inês Domingues; José Pereira Amorim; Pedro H. Abreu (SAC, 2019)

Computer Vision in Esophageal Cancer: A Literature Review. - Inês Domingues; Inês Sampaio; Hugo Duarte; João A. M. Santos; Pedro H. Abreu (IEEE Access, 2019)

Classification of oesophagic early-stage cancers: deep learning versus traditional learning approaches. - Jorge Ferreira; Inês Domingues; Olga Sousa; Inês Sampaio; João A. M. Santos (BIBE, 2020)

Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. - Inês Domingues; Gisèle Pereira; Pedro Martins 003; Hugo Duarte; João A. M. Santos; Pedro Henriques Abreu (Artif. Intell. Rev., 2020)

Multimedia systems and applications in biomedicine. - Inês Domingues; Ana Filipa Sequeira; Carla M. A. Pinto; Álvaro Rocha 001 (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2020)

Shift If You Can: Counting and Visualising Correction Operations for Beat Tracking Evaluation. - António Sá Pinto; Inês Domingues; Matthew E. P. Davies (CoRR, 2020)

Guest Editorial: Information Fusion for Medical Data: Early, Late, and Deep Fusion Methods for Multimodal Data. - Inês Domingues; Henning Müller; Andrés Ortiz 001; Belur V. Dasarathy; Pedro H. Abreu; Vince D. Calhoun (IEEE J. Biomed. Health Informatics, 2020)

Predicting the Use of Invasive Mechanical Ventilation in ICU COVID-19 Patients. - Diana Serrano; Celeste Dias; Bruno Cardoso; Inês Domingues (CIARP, 2021)

An Iterative Algorithm for Semisupervised Classification of Hotspots on Bone Scintigraphies of Patients with Prostate Cancer. - Laura Providência; Inês Domingues; João Santos (J. Imaging, 2021)

Study on Data Partition for Delimitation of Masses in Mammography. - Luís Viegas; Inês Domingues; Mateus Mendes (J. Imaging, 2021)

Knowledge Distillation of Vision Transformers and Convolutional Networks to Predict Inflammatory Bowel Disease. - José Maurício; Inês Domingues (CIARP, 2023)

Deep Neural Networks to Distinguish Between Crohn's Disease and Ulcerative Colitis. - José Maurício; Inês Domingues (IbPRIA, 2023)

Editorial of the Special Issue from WorldCIST'20. - Inês Domingues; Ana Filipa Sequeira (Comput. Math. Organ. Theory, 2023)

Design and Implementation of an Introductory Course on Clinical Databases. - Inês Domingues (EDUCON, 2024)

A Paradigm Shift in Teaching Machine Learning to Sustainable City Management Master Students. - Inês Domingues; Deolinda Maria Lopes Dias Rasteiro (ICETC, 2024)

Distinguishing between Crohn's disease and ulcerative colitis using deep learning models with interpretability. - José Maurício; Inês Domingues (Pattern Anal. Appl., 2024)

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. - Stephanie Batista Niño; Jorge Bernardino; Inês Domingues (Sensors, 2024)

Correction to: Distinguishing between Crohn's disease and ulcerative colitis using deep learning models with interpretability. - José Maurício; Inês Domingues (Pattern Anal. Appl., 2024)

Uplift Modeling for Treatment Effect Estimation in the Prostate Cancer Treatment Landscape. - Ana Rodrigues 007; Nuno Miguel Rodrigues; José Guilherme de Almeida; Ana Mascarenhas Gaivão; Carlos Bilreiro; Inês Santiago; Joana Ip; Sara Belião; Manolis Tsiknakis; Kostas Marias; Daniele Regge; Nikolaos Papanikolaou 003; et al. (IbPRIA (2), 2025)

Deep Learning Techniques for Detecting Morphed Face Images: A Literature Review. - Pedro Morais; Inês Domingues; Jorge Bernardino (IEEE Access, 2025)

Editorial of the special section: CIARP 2023. - Inês Domingues; Verónica Vasconcelos; Simão Paredes (Pattern Recognit. Lett., 2025)