Arne Schmidt

AI Software Engineer

Researcher

Mathematician

I am an enthusiastic, independent, and fast-learning software engineer and researcher specialized in AI for medical images. Several years of hands-on experience, including scrum workflow, CICD, and robust software development are completed by a strong mathematical background. Currently, I am pursuing my Ph.D. within the European project CLARIFY (http://www.clarify-project.eu). I am looking for the next challenge preferably in a collaborative team developing diagnostic tools with a high future impact.

Articles

Submitted

A. Schmidt, P. Morales-Álvarez, L. A. D. Cooper, L. A. Newberg, A. Enquobahrie, R. Molina, and A. K. Katsaggelos, “Focused Active Learning for Histopathological Image Classification”, Medical Image Analysis, 2023.
P. Morales-Álvarez, A. Schmidt, J.M. Hernández-Lobato, and R. Molina, “Introducing instance correlation in multiple instance learning. Application to cancer detection on histopathological images”, Pattern Recognition, May 2022.
J. Pérez-Cano, Y. Wu, A. Schmidt, Miguel López-Pérez, Pablo Morales-Álvarez, Rafael Molina, and Aggelos K. Katsaggelos, “An End-to-end Approach to combine Attention feature extraction and Gaussian Process models for Deep Multiple Instance Learning”, Expert Systems With Applications, 2022.

Accepted

A. Schmidt, P. Morales-Álvarez, and R. Molina, “Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation”, The International Conference on Computer Vision (ICCV), 2023.

Published

A. Schmidt, P. Morales-Álvarez, and R. Molina, “Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning”, IEEE Transactions on Neural Networks and Learning Systems, 2023.
A. Schmidt, J. Silva-Rodríguez, R. Molina, and V. Naranjo, “Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning”, IEEE Access, vol. 10, 9763-9773, January 2022.
J. Silva-Rodríguez, A. Schmidt, M. A. Sales, R. Molina, and V. Naranjo, “Proportion constrained weakly supervised histopathology image classification”, Computers in Biology and Medicine, 2022.
M. López-Pérez, A. Schmidt, Y. Wu, R. Molina, and A.K. Katsaggelos, “Deep Gaussian Processes for Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection”, Computer Methods and Programs in Biomedicine, vol. 219, 106783, June 2022.
N. Kanwal, F. Pérez-Bueno, A. Schmidt, K. Engan, and R. Molina, “The devil is in the details: Whole Slide Image acquisition and processing for artifact detection, color variation, and data augmentation. A review.”, IEEE Access, vol. 10, 58821-58844, May 2022.
Y. Wu, A. Schmidt, E. Hernández-Sánchez, R. Molina, and A. K. Katsaggelos, “Combining Attention-based Multiple Instance Learning and Gaussian Processes for CTHemorrhage Detection” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2021.