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Related Concept Videos

Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Related Experiment Video

Updated: Jun 25, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Tissue concepts: Supervised foundation models in computational pathology.

Till Nicke1, Jan Raphael Schäfer1, Henning Höfener1

  • 1Fraunhofer Institute for Digital Medicine MEVIS, Bremen/Lübeck/Aachen, Germany.

Computers in Biology and Medicine
|January 10, 2025
PubMed
Summary

A new supervised training method significantly reduces the cost of developing artificial intelligence (AI) foundation models for pathology. This approach trains a joint encoder using multi-task learning, achieving high performance with less data and computation.

Keywords:
Computational pathologyFoundation modelsMulti-task learningRepresentation learning

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Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Computational biology

Background:

  • Pathologist workload is increasing, driving the need for automated diagnostic support.
  • Foundation models offer generalizability but are expensive to train.
  • Data-efficient development of specialized AI models is crucial.

Purpose of the Study:

  • To propose a supervised training method for foundation models that drastically reduces data, computation, and time expenses.
  • To introduce the Tissue Concepts encoder, trained via multi-task learning.
  • To evaluate the encoder's performance and generalizability across centers.

Main Methods:

  • Multi-task learning to train a joint encoder on 912,000 patches.
  • Combined 16 classification, segmentation, and detection tasks.
  • Evaluated on whole slide images of breast, colon, lung, and prostate cancer.

Main Results:

  • Tissue Concepts model achieved comparable performance to self-supervised models using only 6% of training patches.
  • Outperformed an ImageNet pre-trained encoder on in-domain and out-of-domain data.
  • Demonstrated strong generalizability across different cancer types and centers.

Conclusions:

  • The proposed supervised multi-task learning method offers a cost-effective approach to training foundation models in digital pathology.
  • The Tissue Concepts encoder shows significant potential for improving AI model development in cancer diagnostics.
  • The method enables robust and generalizable AI tools for pathologists.