I am a Postdoctoral Researcher at the Institute of Biomedicine, University of Turku, Finland, developing self-supervised foundation models for whole-slide image analysis in computational pathology. My current work focuses on building models that can improve cancer diagnosis and prognosis,  using histopathology data. I received my PhD in Informatics from The University of Electro-Communications, Tokyo, where my research centered on deep learning methods for 2D hand pose estimation using graph convolutional networks and attention mechanisms. My research interests include deep learning, computer vision, medical image analysis, and transfer learning

My research focuses on developing self-supervised foundation models for computational histopathology, with applications in cancer diagnosis, prognosis, and prediction of treatment response from whole-slide images and tissue microarray spots. I am interested in developing methods that reduce the need for expensive manual annotations in histopathology, enabling downstream tasks such as tumor classification, biomarker prediction, and survival analysis. My work investigates how large-scale pretraining on unlabeled histopathology data can enhance model generalization across various cancer types, tissue samples, and clinical endpoints. I am also interested in understanding how these foundation models can be adapted efficiently to new tasks with minimal labeled data. During my PhD at The University of Electro-Communications, Tokyo, I developed deep learning methods for 2D hand pose estimation using graph convolutional networks, deformable convolutions, and attention mechanisms. My broader research interests include computer vision, medical image analysis, transformers, transfer learning, and applying deep learning to address real-world healthcare challenges.