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Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

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Related Experiment Videos

Frequency-Aware Causal Regularization for Multiple Instance Learning in Whole Slide Image Classification.

Dawei Fan, Lifang Wei, Mingyue Han

    IEEE Transactions on Medical Imaging
    |May 26, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces frequency-aware causal regularized multiple instance learning (FC-MIL) for whole slide image classification. FC-MIL enhances diagnostic accuracy and interpretability by combining spatial-frequency features and causal regularization.

    Related Experiment Videos

    Area of Science:

    • Computational pathology
    • Digital pathology
    • Artificial intelligence in medicine

    Background:

    • Whole slide image (WSI) classification is crucial for automated diagnostics in computational pathology.
    • Traditional multiple instance learning (MIL) methods struggle with attention misalignments and reliance on spurious correlations.
    • This limits the reliability of automated histopathological diagnoses.

    Purpose of the Study:

    • To develop an improved weakly supervised learning framework for WSI classification.
    • To enhance the accuracy and interpretability of automated diagnostic support systems.
    • To address the limitations of traditional MIL methods in pathological analysis.

    Main Methods:

    • Proposed frequency-aware causal regularized multiple instance learning (FC-MIL) framework.
    • Frequency-aware attention (FAA) integrates spatial and frequency domain features for finer texture analysis.
    • Causal regularization (CR) uses counterfactual perturbations to reduce reliance on spurious correlations.

    Main Results:

    • FC-MIL demonstrated superior performance over state-of-the-art MIL methods on four WSI datasets.
    • The framework achieved improvements in both classification accuracy and interpretability.
    • FAA and CR effectively guided the model towards invariant pathological cues.

    Conclusions:

    • FC-MIL offers a robust and interpretable approach for weakly supervised WSI classification.
    • The integration of frequency-aware attention and causal regularization significantly enhances diagnostic reliability.
    • This framework advances automated diagnostic support in computational pathology.