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168,742 papers · 148 categories

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0111 · May 201619922001200920172026
7 results for intestine

Deep learning model diagnoses celiac disease severity from intestinal biopsy images.

problem Diagnosing celiac disease severity from biopsy images, especially mild cases.
method Deep residual networks trained on a modified Marsh score histological scoring system.
result Model achieved AUC > 0.96 in all classes for CD severity classification.

Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.

problem Time-consuming and high error rate in manual inspection of large numbers of similar capsule endoscopic images.
method Structural similarity analysis of visually salient areas and hierarchical clustering.
result 76% reduction in similar images, 100% lesion recall, 18-minute average play time.

Semi-automatic data annotation helps experts label unlabeled samples based on feature space projection.

problem Laborious manual data annotation for machine learning.
method Interactive semi-automatic approach using feature space projection and semi-supervised learning.
result Reduces user annotation effort and improves classification accuracy.

Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels o…

2016-05-16abs ↗pdf ↗

Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.

problem Influenza disease classification from scRNA-seq data is prone to donor-level confounding.
method Developed and evaluated three feature representations across two IBD cohorts.
result Compartment-stratified CLR composition and GatedStructuralCFN embeddings outperform linear models in classification accuracy.

Study proposes deep learning techniques to diagnose and differentiate Celiac Disease and Environmental Enteropathy from biopsy images.

problem Challenging histopathologic overlap between Celiac Disease and Environmental Enteropathy in biopsy images.
method Color balancing and Random Multimodel Deep Learning (RMDL) architecture to address staining variability.
result Proposed deep learning techniques improve diagnosis accuracy of Celiac Disease and Environmental Enteropathy.