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Found 15 papers, 4 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.
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.
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
The model has two key designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive learning (CRC) module.
ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers
Automated diagnosis of Alzheimer Disease(AD) from brain imaging, such as magnetic resonance imaging (MRI), has become increasingly important and has attracted the community to contribute many deep learning methods.
Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement
As a mainstream signal processing method, wavelet is hailed as the mathematical microscope of signal due to the plentiful and diverse wavelet basis functions.
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).
Rapid Image Labeling via Neuro-Symbolic Learning
To address this challenge, we propose a neuro-symbolic approach called Rapid, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules.
Learning Robust Medical Image Segmentation from Multi-source Annotations
Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images.
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image
Moreover, we highlight AugDiff's higher-quality augmented feature over image augmentation and its superiority over self-supervised learning.
LNPL-MIL: Learning from Noisy Pseudo Labels for Promoting Multiple Instance Learning in Whole Slide Image
Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis analysis are promising directions in computational pathology.
Human Health Indicator Prediction from Gait Video
To address this issue, we analyse the similarity and relationship between pose estimation and health indicator prediction tasks, and then propose a paradigm enabling deep learning for small health indicator datasets by pre-training on the pose estimation task.
Multiple Instance Learning with Mixed Supervision in Gleason Grading
We achieve the state-of-the-art performance on the SICAPv2 dataset, and the visual analysis shows the accurate prediction results of instance level.
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis.
An Equation for Predicting Binding Strengths of Metal Cations to Protein of Human Serum Transferrin
Because human serum transferrin (hTF) exists freely in serum, it is a potential target for cancer treatment drugs and in curing iron-overloaded conditions in patients via long-term transfusion therapy.
A Novel Approach for Stable Selection of Informative Redundant Features from High Dimensional fMRI Data
Feature selection is among the most important components because it not only helps enhance the classification accuracy, but also or even more important provides potential biomarker discovery.
