Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

356
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...
356
Classification of Systems-II01:31

Classification of Systems-II

192
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,
192
Associative Learning01:27

Associative Learning

474
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...
474
Classification of Systems-I01:26

Classification of Systems-I

236
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:
236
Force Classification01:22

Force Classification

1.3K
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,...
1.3K
Observational Learning01:12

Observational Learning

250
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
250

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Advanced interfacial design of hempseed oil bodies via pH-ultrasound coupling: From molecular rearrangement to emulsion stabilization.

Food chemistry·2026
Same author

Association between prognostic nutritional index (PNI)/Controlling Nutritional Status (CONUT) score and prognosis in patients with bladder cancer: a systematic review and meta-analysis.

Translational andrology and urology·2026
Same author

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EVDI++: Event-based Video Deblurring and Interpolation via Self-Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

IHM-DDI: Integrating high-order interaction topology and Morgan fingerprint features for drug-drug interaction prediction.

Analytical biochemistry·2026
Same author

Developing SHAP interpretable machine learning models for assessing biopsychosocial risk in female drug users: a small sample study.

Frontiers in psychiatry·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Aug 4, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Bayesian Collaborative Learning for Whole-Slide Image Classification.

Jin-Gang Yu, Zihao Wu, Yu Ming

    IEEE Transactions on Medical Imaging
    |April 6, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Bayesian Collaborative Learning (BCL) addresses whole-slide image classification challenges by using an auxiliary patch classifier to collaboratively train feature encoders and MIL aggregators, overcoming memory bottlenecks and improving performance.

    More Related Videos

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.5K
    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
    06:19

    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

    Published on: August 16, 2024

    459

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    11.9K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.5K
    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
    06:19

    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

    Published on: August 16, 2024

    459

    Area of Science:

    • Computational pathology
    • Digital pathology
    • Machine learning in healthcare

    Background:

    • Whole-slide image (WSI) classification is crucial for computational pathology but faces challenges like high resolution and data heterogeneity.
    • Existing Multiple Instance Learning (MIL) methods for WSI classification are hindered by memory bottlenecks due to gigapixel image sizes.
    • Decoupling feature encoders and MIL aggregators in conventional MIL networks often leads to performance degradation.

    Purpose of the Study:

    • To introduce a novel Bayesian Collaborative Learning (BCL) framework to overcome memory limitations in WSI classification.
    • To enable collaborative learning between feature encoders and MIL aggregators within MIL networks.
    • To improve the performance of WSI classification by addressing inherent memory bottlenecks.

    Main Methods:

    • Developed a Bayesian Collaborative Learning (BCL) framework incorporating an auxiliary patch classifier.
    • Formulated a unified Bayesian probabilistic framework for collaborative learning.
    • Implemented an Expectation-Maximization algorithm for iterative parameter inference, including a quality-aware pseudo labeling strategy for the E-step.

    Main Results:

    • Achieved Area Under the Curve (AUC) scores of 95.6% on CAMELYON16, 96.0% on TCGA-NSCLC, and 97.5% on TCGA-RCC.
    • Demonstrated consistent performance superior to all compared methods across three public WSI datasets.
    • Validated the effectiveness of the BCL framework in addressing memory bottlenecks and enhancing WSI classification accuracy.

    Conclusions:

    • The proposed Bayesian Collaborative Learning (BCL) framework effectively resolves the memory bottleneck issue in whole-slide image classification.
    • BCL enables collaborative training of feature encoders and MIL aggregators, leading to state-of-the-art performance.
    • The framework offers a promising direction for advancing computational pathology through efficient and accurate WSI analysis.