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 …
Scaling self-supervised learning for histopathology with masked image modeling
Computational pathology is revolutionizing the field of pathology by integrating advanced
computer vision and machine learning technologies into diagnostic workflows. It offers …
computer vision and machine learning technologies into diagnostic workflows. It offers …
In-context learning enables multimodal large language models to classify cancer pathology images
Medical image classification requires labeled, task-specific datasets which are used to train
deep learning networks de novo, or to fine-tune foundation models. However, this process is …
deep learning networks de novo, or to fine-tune foundation models. However, this process is …
Mambamil: Enhancing long sequence modeling with sequence reordering in computational pathology
Abstract Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract
discriminative feature representations within Whole Slide Images (WSIs) in computational …
discriminative feature representations within Whole Slide Images (WSIs) in computational …
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 - ar** 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 …