131 citations · 145 across the 4 of their papers we have counts for
6 papers
HistoEncoder: a digital pathology foundation model for prostate cancer
Joona Pohjonen, Abderrahim-Oussama Batouche, Antti Rannikko +4
Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources…
Synergizing Data Imputation and Electronic Health Records for Advancing Prostate Cancer Research: Challenges, and Practical Applications
Abderrahim Oussama Batouche, Eugen Czeizler, Miika Koskinen +2
The presence of detailed clinical information in electronic health record (EHR) systems presents promising prospects for enhancing patient care through automated retrieval techniqu…
Classification of datasets with imputed missing values: does imputation quality matter?
Tolou Shadbahr, Michael Roberts, Jan Stanczuk +15
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missi…
Augment like there's no tomorrow: Consistently performing neural networks for medical imaging
Joona Pohjonen, Carolin Stürenberg, Atte Föhr +6
Deep neural networks have achieved impressive performance in a wide variety of medical imaging tasks. However, these models often fail on data not used during training, such as dat…
Spectral decoupling allows training transferable neural networks in medical imaging
Joona Pohjonen, Carolin Stürenberg, Antti Rannikko +2
Many current neural networks for medical imaging generalise poorly to data unseen during training. Such behaviour can be caused by networks overfitting easy-to-learn, or statistica…
Improving Prostate Cancer Detection with Breast Histopathology Images
Umair Akhtar Hasan Khan, Carolin Stürenberg, Oguzhan Gencoglu +4
Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the…