https://dasya.itu.dk/tags/transfer-learning/ Recent content in transfer learning on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Thu, 11 May 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ajs-medical-vqa/ Thu, 11 May 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ajs-medical-vqa/ A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is obtained for medical datasets. Recent studies show that even with high performance on the existing data, algorithms can learn “shortcuts” like visibility of medical tools and fail to generalize. https://dasya.itu.dk/for-students/proposals/archive/dj-pretraining_strategies/ Mon, 30 May 2022 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/dj-pretraining_strategies/ Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such cases, transfer learning is often used. A most common approach is to use a supervised classifier trained on ImageNet (or any other available large dataset) and finetune its parameters using the target dataset such as clinical images.