Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into...
Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-width, which plays a crucial role in the fast-growing AI industry. Despite model quantization's success ...
Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficienc...
Recent unsupervised domain adaptation (UDA) methods in medical image segmentation commonly utilize Generative Adversarial Networks (GANs) for domain translation. However, the translated images often exhibit a distribution deviation from the ideal due...
Fundus image quality serves a crucial asset for medical diagnosis and applications. However, such images often suffer degradation during image acquisition where multiple types of degradation can occur in each image. Although recent deep learning base...
Landmark detection is a crucial task in medical image analysis, with applications across various fields. However, current methods struggle to accurately locate landmarks in medical images with blurred tissue boundaries due to low image quality. In pa...
Natural Image Captioning (NIC) is an interdisciplinary research area that lies within the intersection of Computer Vision (CV) and Natural Language Processing (NLP). Several works have been presented on the subject, ranging from the early template-ba...
State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervise...
The lack of large datasets and high-quality annotated data often limits the development of accurate and robust machine-learning models within the medical and surgical domains. In the machine learning community, generative models have recently demonst...
Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training dat...