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Found 16 papers, 3 papers with code
Diffusion and Multi-Domain Adaptation Methods for Eosinophil Segmentation
Eosinophilic Esophagitis (EoE) represents a challenging condition for medical providers today.
Uncertainty Quantification for Eosinophil Segmentation
The uncertainty can be visualized in an output image to evaluate model performance, provide insight to how deep learning algorithms function, and assist pathologists in identifying eosinophils.
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.
MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation
Nuclei vary substantially in structure and appearances across different cancer types, leading to a drop in performance of deep learning models when trained on one cancer type and tested on another.
Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos
In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels.
Unsupervised Shot Boundary Detection for Temporal Segmentation of Long Capsule Endoscopy Videos
However, the search for temporal boundaries in a long video using high dimensional frame-feature matrix is computationally prohibitive and impracticable for real clinical application.
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.
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video
Automating the detection of frames containing specific lesion in WCE video would relieve gastroenterologists the arduous task of reviewing the entire video before making diagnosis.
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.
Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease
Physicians use biopsies to distinguish between different but histologically similar enteropathies.
Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks
We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images.
