Transformer-based unsupervised contrastive learning for histopathological image classification
A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …
medical image analysis. However, assembling such large annotations is very challenging …
Vision transformers in medical imaging: A review
EU Henry, O Emebob, CA Omonhinmin - arXiv preprint arXiv:2211.10043, 2022 - arxiv.org
Transformer, a model comprising attention-based encoder-decoder architecture, have
gained prevalence in the field of natural language processing (NLP) and recently influenced …
gained prevalence in the field of natural language processing (NLP) and recently influenced …
Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis
F Haghighi, MRH Taher… - Proceedings of the …, 2022 - openaccess.thecvf.com
Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging …
beneficial for self-supervised learning schemes in computer vision and medical imaging …
Dive into the details of self-supervised learning for medical image analysis
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific …
Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
[HTML][HTML] Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images
COVID-19 cases are putting pressure on healthcare systems all around the world. Due to
the lack of available testing kits, it is impractical for screening every patient with a respiratory …
the lack of available testing kits, it is impractical for screening every patient with a respiratory …
Robust and efficient medical imaging with self-supervision
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …
clinical expert level performance. However, such systems tend to demonstrate sub-optimal" …
[HTML][HTML] Swincup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer
Transformer models have recently become the dominant architecture in many computer
vision tasks, including image classification, object detection, and image segmentation. The …
vision tasks, including image classification, object detection, and image segmentation. The …
New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review
X Ge, J Gao, R Niu, Y Shi, X Shao, Y Wang… - Frontiers in …, 2023 - frontiersin.org
Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of
cancer-associated deaths. In recent years, significant progress has been achieved in basic …
cancer-associated deaths. In recent years, significant progress has been achieved in basic …
Caid: Context-aware instance discrimination for self-supervised learning in medical imaging
MRH Taher, F Haghighi… - … on Medical Imaging …, 2022 - proceedings.mlr.press
Recently, self-supervised instance discrimination methods have achieved significant
success in learning visual representations from unlabeled photographic images. However …
success in learning visual representations from unlabeled photographic images. However …