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Concepts and Prototypes01:24

Concepts and Prototypes

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
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Imaging Studies for Cardiovascular System III: X-Ray01:20

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Counterfactual Thinking01:19

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Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in...
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Storage01:23

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Radiological Investigation I: X-ray and CT01:30

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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Related Experiment Video

Updated: Mar 13, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
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Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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An Interpretable Chest X-ray Classification Framework Using Prototype Memory and Counterfactual Consistency.

Ling-Feng Chiang1

  • 1Internet of Things (IoT) Engineering and Applications, Yu Da University of Science and Technology, Miaoli County, TWN.

Cureus
|March 12, 2026
PubMed
Summary
This summary is machine-generated.

CXR-NeXus enhances chest X-ray analysis by using prototype memory and counterfactual consistency for reliable, interpretable classification. This deep learning framework ensures models focus on relevant pulmonary evidence, improving diagnostic accuracy.

Keywords:
chest x-ray classificationcounterfactual consistencyinterpretable deep learningmedical image reliabilityprototype memory networkpulmonary imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Chest X-ray (CXR) interpretation is complex, with deep learning models often failing due to reliance on irrelevant image features.
  • Lack of interpretability and reliance on spurious cues hinder clinical adoption of AI in radiology.

Purpose of the Study:

  • To introduce CXR-NeXus, an interpretable deep learning framework for chest X-ray classification.
  • To promote explicit visual reasoning and reliable decision-making using weak supervision.

Main Methods:

  • Integrates prototype memory and counterfactual consistency for clinically meaningful evidence.
  • Learns class-specific prototypes representing diverse radiographic phenotypes (COVID-19, pneumonia, tuberculosis, normal).
  • Employs Grad-CAM-guided lesion suppression for counterfactual generation and evidence-alignment regularization.

Main Results:

  • Improved macro-average F1 score, ROC-AUC, specificity, and probability calibration on a four-class CXR dataset.
  • Significantly reduced reliance on spurious visual cues compared to baseline models.
  • Demonstrated transparent "this-looks-like-that" explanations and anatomically plausible attention.

Conclusions:

  • CXR-NeXus offers a practical and interpretable solution for reliable chest X-ray analysis.
  • Combining prototype-level semantic anchoring with counterfactual reasoning enhances model robustness and clinical relevance.
  • The framework achieves high performance without requiring pixel-level annotations.