Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
[HTML][HTML] Transformers in medical image analysis
Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …
made an impact in the area of computer vision. In the field of medical image analysis …
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 …
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Benchmarking self-supervised learning on diverse pathology datasets
Computational pathology can lead to saving human lives, but models are annotation hungry
and pathology images are notoriously expensive to annotate. Self-supervised learning has …
and pathology images are notoriously expensive to annotate. Self-supervised learning has …
Node-aligned graph convolutional network for whole-slide image representation and classification
The large-scale whole-slide images (WSIs) facilitate the learning-based computational
pathology methods. However, the gigapixel size of WSIs makes it hard to train a …
pathology methods. However, the gigapixel size of WSIs makes it hard to train a …
Self-supervised vision transformers learn visual concepts in histopathology
RJ Chen, RG Krishnan - arXiv preprint arXiv:2203.00585, 2022 - arxiv.org
Tissue phenotyping is a fundamental task in learning objective characterizations of
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology …
A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images
The tumor grading of laryngeal cancer pathological images needs to be accurate and
interpretable. The deep learning model based on the attention mechanism-integrated …
interpretable. The deep learning model based on the attention mechanism-integrated …
Modeling dense multimodal interactions between biological pathways and histology for survival prediction
Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival
can improve our understanding of patient prognosis. However this multimodal task is …
can improve our understanding of patient prognosis. However this multimodal task is …
A survey on artificial intelligence in histopathology image analysis
The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
dramatically transformed pathologists' workflow and allowed the use of computer systems in …