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 …
One label is all you need: Interpretable AI-enhanced histopathology for oncology
Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to
benefit oncology through interpretable methods that require only one overall label per …
benefit oncology through interpretable methods that require only one overall label per …
How2comm: Communication-efficient and collaboration-pragmatic multi-agent perception
Multi-agent collaborative perception has recently received widespread attention as an
emerging application in driving scenarios. Despite the advancements in previous efforts …
emerging application in driving scenarios. Despite the advancements in previous efforts …
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 …
Boosting whole slide image classification from the perspectives of distribution, correlation and magnification
Bag-based multiple instance learning (MIL) methods have become the mainstream for
Whole Slide Image (WSI) classification. However, there are still three important issues that …
Whole Slide Image (WSI) classification. However, there are still three important issues that …
Rethinking multiple instance learning for whole slide image classification: A good instance classifier is all you need
Weakly supervised whole slide image classification is usually formulated as a multiple
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …
instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut …
Contrastive multiple instance learning: An unsupervised framework for learning slide-level representations of whole slide histopathology images without labels
Simple Summary Recent AI methods in the automated analysis of histopathological imaging
data associated with cancer have trended towards less supervision by humans. Yet, there …
data associated with cancer have trended towards less supervision by humans. Yet, there …
Trans2Fuse: Empowering image fusion through self-supervised learning and multi-modal transformations via transformer networks
Image fusion enhances a single image by integrating information from multiple sources with
complementary data. Present end-to-end fusion methods often face overfitting or intricate …
complementary data. Present end-to-end fusion methods often face overfitting or intricate …
Kernel attention transformer for histopathology whole slide image analysis and assistant cancer diagnosis
Transformer has been widely used in histopathology whole slide image analysis. However,
the design of token-wise self-attention and positional embedding strategy in the common …
the design of token-wise self-attention and positional embedding strategy in the common …
The rise of ai language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification
This paper introduces the novel concept of few-shot weakly supervised learning for
pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed …
pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed …