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Found 8 papers, 2 papers with code
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning
Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models.
HistoTransfer: Understanding Transfer Learning for Histopathology
In this work, we compare the performance of features extracted from networks trained on ImageNet and histopathology data.
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
We regularized the clustering mechanism by introducing a KL-divergence loss between the attention weights of patches in a cluster and the uniform distribution.
Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision
A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression.
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach
Image classification is central to the big data revolution in medicine.
Hierarchical Deep Convolutional Neural Networks for Multi-category Diagnosis of Gastrointestinal Disorders on Histopathological Images
The results indicate that the hierarchical model can achieve better results than the flat model for multi-category diagnosis of GI disorders using histopathological images.
CeliacNet: Celiac Disease Severity Diagnosis on Duodenal Histopathological Images Using Deep Residual Networks
These results demonstrate the diagnostic power of the proposed model for CD severity classification using histological images.
Self-Attentive Adversarial Stain Normalization
We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain.
