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Found 6 papers, 1 papers with code
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
However, the current weakly supervised nuclei segmentation approaches typically follow a two-stage pseudo-label generation and network training process.
AV-GAN: Attention-Based Varifocal Generative Adversarial Network for Uneven Medical Image Translation
In this paper, we propose the Attention-Based Varifocal Generative Adversarial Network (AV-GAN), which solves multiple problems in pathologic image translation tasks, such as uneven translation difficulty in different regions, mutual interference of multiple resolution information, and nuclear deformation.
MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models
To tackle this challenge, we propose a MamMIL framework for WSI classification by cooperating the selective structured state space model (i. e., Mamba) with MIL for the first time, enabling the modeling of instance dependencies while maintaining linear complexity.
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma
Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG).
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
