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Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
A visual–language foundation model for pathology image analysis using medical twitter
The lack of annotated publicly available medical images is a major barrier for computational
research and education innovations. At the same time, many de-identified images and much …
research and education innovations. At the same time, many de-identified images and much …
A foundation model for clinical-grade computational pathology and rare cancers detection
The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …
decision support systems and precision medicine. The success of such applications …
A visual-language foundation model for computational pathology
The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …
the development of robust models for various pathology tasks across a diverse array of …
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 …
Transfer learning for medical image classification: a literature review
Background Transfer learning (TL) with convolutional neural networks aims to improve
performances on a new task by leveraging the knowledge of similar tasks learned in …
performances on a new task by leveraging the knowledge of similar tasks learned in …
MedViT: a robust vision transformer for generalized medical image classification
Abstract Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the reliability of …
for automatic disease diagnosis. However, there are still concerns about the reliability of …
Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …