Interventional bag multi-instance learning on whole-slide pathological images
Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …
Setmil: spatial encoding transformer-based multiple instance learning for pathological image analysis
Considering the huge size of the gigapixel whole slide image (WSI), multiple instance
learning (MIL) is normally employed to address pathological image analysis tasks, where …
learning (MIL) is normally employed to address pathological image analysis tasks, where …
Detection of breast cancer from whole slide histopathological images using deep multiple instance CNN
Histopathological Whole Slide Imaging (WSI) has become a standard in the detection of
breast cancer. Automated image analysis methods attempt to reduce the workload from the …
breast cancer. Automated image analysis methods attempt to reduce the workload from the …
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis.
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …
However, due to the large scale of WSIs and various sizes of the abnormal area, how to …
Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering
Cancer tissues in histopathology images exhibit abnormal patterns; it is of great clinical
importance to label a histopathology image as having cancerous regions or not and perform …
importance to label a histopathology image as having cancerous regions or not and perform …
MuRCL: Multi-instance reinforcement contrastive learning for whole slide image classification
Multi-instance learning (MIL) is widely adop-ted for automatic whole slide image (WSI)
analysis and it usually consists of two stages, ie, instance feature extraction and feature …
analysis and it usually consists of two stages, ie, instance feature extraction and feature …
Graph attention multi-instance learning for accurate colorectal cancer staging
Colorectal Cancer (CRC) is one of the most common cancer diagnosed in humans.
Outcomes vary significantly among patients with different tumor status. Accurate staging of …
Outcomes vary significantly among patients with different tumor status. Accurate staging of …
Cancer survival prediction from whole slide images with self-supervised learning and slide consistency
L Fan, A Sowmya, E Meijering… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Histopathological Whole Slide Images (WSIs) at giga-pixel resolution are the gold standard
for cancer analysis and prognosis. Due to the scarcity of pixel-or patch-level annotations of …
for cancer analysis and prognosis. Due to the scarcity of pixel-or patch-level annotations of …
Generating hypergraph-based high-order representations of whole-slide histopathological images for survival prediction
Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs)
has become increasingly prevalent in recent years. A key challenge of this task is achieving …
has become increasingly prevalent in recent years. A key challenge of this task is achieving …
A multi-resolution model for histopathology image classification and localization with multiple instance learning
Large numbers of histopathological images have been digitized into high resolution whole
slide images, opening opportunities in developing computational image analysis tools to …
slide images, opening opportunities in developing computational image analysis tools to …