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Related Concept Videos

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
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Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Classification of Systems-I01:26

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Related Experiment Video

Updated: May 23, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

RetCond: A Conditional Diffusion Model for Self-Explanatory Multi-Class Fundus Image Classification.

Ahmad O Ahsan1, Christopher Nielsen2,3,4,5, Raissa Souza2,3,4,5

  • 1Biomedical Engineering Graduate Program, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada. ahmad.omarahsan@ucalgary.ca.

Journal of Medical Systems
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

RetCond, a novel AI tool, accurately detects retinal diseases using generative classification and provides visual explanations. This approach enhances trust in AI for ophthalmology by offering transparent diagnostic insights.

Keywords:
CounterfactualsDeep learningDiffusionFundusGenerative classifier

Related Experiment Videos

Last Updated: May 23, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Vision impairment is a global health issue, necessitating early retinal disease diagnosis.
  • Manual interpretation of color fundus photography is time-consuming and variable.
  • Current deep learning models for retinal disease detection often lack transparency.

Purpose of the Study:

  • To introduce RetCond, a self-explanatory generative classifier for retinal disease detection.
  • To provide accurate predictions with built-in, counterfactual visual explanations.
  • To enhance trust and transparency in AI solutions for ophthalmology.

Main Methods:

  • RetCond, a diffusion model repurposed as a classifier, generates counterfactual images for explanations.
  • A diverse dataset of 19,565 retinal images across five disease conditions was curated.
  • Performance was evaluated using standard classification metrics and qualitative/quantitative analysis of explanations.

Main Results:

  • RetCond achieved 96.98% classification accuracy, comparable to state-of-the-art models.
  • The model generated condition-specific counterfactual images, confirming its learned concepts.
  • RetCond demonstrated self-explanatory properties, highlighting its decision-making process.

Conclusions:

  • RetCond offers trustworthy AI in ophthalmology by matching discriminative performance with transparency.
  • The generative classifier addresses limitations of post-hoc interpretability in deep learning.
  • Self-explanatory AI models like RetCond are crucial for reliable automated retinal disease diagnosis.