Related Experiment Video
Updated: Jan 21, 2026
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.9K
Creating the Black Box: A Primer on Convolutional Neural Network Use in Image Interpretation
Toshimasa Clark1, Eric Nyberg1
1University of Colorado Anschutz Medical Campus, Aurora, CO.
Current Problems in Diagnostic Radiology
|July 22, 2019
Summary
Radiologists can use transfer learning to easily implement advanced convolutional neural networks for medical image analysis. This approach offers high diagnostic performance without requiring deep computer science expertise.
Area of Science:
- Radiology
- Artificial Intelligence
- Medical Imaging
Background:
- Convolutional neural networks (CNNs) show high diagnostic performance in radiology.
- CNNs can be complex for non-computer science experts.
- Transfer learning offers a simpler implementation method.
Purpose of the Study:
- To illustrate how radiologists can set up state-of-the-art CNNs for image interpretation using transfer learning.
- To provide an accessible method for radiologists to utilize advanced AI tools.
Main Methods:
- Utilizing transfer learning to adapt pre-trained CNNs for radiologic tasks.
- Demonstrating the implementation process for radiologists without deep computer science backgrounds.
Main Results:
- Transfer learning is relatively simple to implement.
- CNNs implemented via transfer learning demonstrate performance equivalent to those trained on specific medical data.
- This method allows radiologists to engage with AI without being overwhelmed.
Conclusions:
- Transfer learning empowers radiologists to leverage powerful CNNs for image interpretation.
- This technique democratizes access to advanced AI in radiology.
- It bridges the gap between clinical practice and AI development.
More Related Videos
Related Concept Videos
01:17Convolution Properties II
582
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
582
01:20Convolution Properties I
562
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
562
01:10Detection of Black Holes
2.5K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.5K
02:26Protein Networks
4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
01:22Interpreting R Charts
343
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
343
01:25Interpreting Run Charts
3.0K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
3.0K