This work presents a novel two-stage shuffle attention MIL (SAMIL) model for breast cancer WSI classification.
Multiple instance learning (MIL) has recently become a powerful tool to solve the weakly supervised classification problem on whole slide image (WSI) ...
SAMIL first introduces shuffle attention to extract important features from both spatial and channel dimensions, which well includes pixel-level pairwise ...
The combination of MIL with the triple kernel, [5], allows for a more refined manipulation of image features, and the generation of an attention map by kernel ...
Nov 13, 2023 · To mitigate overfitting, we present Attention-Challenging MIL (ACMIL). ACMIL combines two techniques based on separate analyses for attention ...
Missing: Shuffle Breast Cancer
In the application of Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) classification, attention mechanisms often focus on a subset of ...
Missing: Shuffle | Show results with:Shuffle
A novel deep learning model, called DRDA-Net, is proposed. DRDA-Net is used for breast cancer classification in histopathological images.
We introduce a novel interpretable decision-support model using CNN with a trainable attention mechanism using response-based feed-forward visual explanation.
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks · Medicine, Computer Science. Medical Image ...
In the application of Multiple Instance Learning (MIL) meth- ods for Whole Slide Image (WSI) classification, attention mechanisms of- ten focus on a subset ...
Missing: Shuffle | Show results with:Shuffle