Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
Multiple instance learning framework with masked hard instance mining for whole slide image classification
The whole slide image (WSI) classification is often formulated as a multiple instance
learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI …
learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI …
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 …
A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images
Cancer region detection (CRD) and subtyping are two fundamental tasks in digital pathology
image analysis. The development of data-driven models for CRD and subtyping on whole …
image analysis. The development of data-driven models for CRD and subtyping on whole …
Lnpl-mil: Learning from noisy pseudo labels for promoting multiple instance learning in whole slide image
Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …
analysis are promising directions in computational pathology. However, limited by …
Bi-directional weakly supervised knowledge distillation for whole slide image classification
Computer-aided pathology diagnosis based on the classification of Whole Slide Image
(WSI) plays an important role in clinical practice, and it is often formulated as a weakly …
(WSI) plays an important role in clinical practice, and it is often formulated as a weakly …
Vision transformers for computational histopathology
Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …
information contained in gigabyte whole slide images, aiming at providing cancer patients …
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Dgmil: Distribution guided multiple instance learning for whole slide image classification
Abstract Multiple Instance Learning (MIL) is widely used in analyzing histopathological
Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data …
Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data …