Data

Tools

Indexes

Applications

Experiments

Open Source Science

3d-viz-score-cam

Visualizing 3D ResNet for Medical Image Classification With Score-CAM

https://github.com/reshalfahsi/3d-viz-score-cam

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.4%) to scientific vocabulary

Keywords

3d 3d-medical-images 3d-resnet cnn-visualization explainability explainable-ai gradient-free image-classification k3d medical-image-classification medmnist pytorch-lightning score-cam torch visualization
Last synced: 6 months ago · JSON representation ·

Repository

Visualizing 3D ResNet for Medical Image Classification With Score-CAM

Basic Info
  • Host: GitHub
  • Owner: reshalfahsi
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 7.06 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
3d 3d-medical-images 3d-resnet cnn-visualization explainability explainable-ai gradient-free image-classification k3d medical-image-classification medmnist pytorch-lightning score-cam torch visualization
Created almost 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Citation

README.md

Visualizing 3D ResNet for Medical Image Classification With Score-CAM

colab

architecture Live interaction with the 3D Score-CAM result.

Intepretability is one of the concerns regarding the application of AI, or, to be exact, deep learning, in the medical field, especially medical image recognition. In a venture seeking to explain what is going on or what the network perceives computationally, one can leverage the class activation map (CAM) of the model. Score-CAM, one of the CAM variants, breakthroughs the preceding CAM methods by dropping the reliance on gradients. Instead, it benefits the full potential of the forward propagation of the model, running inference by normalized-weighting via element-wise product with the input. Then, the output logit of the target category is combined with the CAM acquired before to get the final outcome. In this project, the 3D version of ResNet is employed. To evaluate the aforementioned methods, the OrganMNIST3D dataset of MedMNIST is used. The deletion area under the curve (DAUC) and the insertion area under the curve (IAUC) are adopted to measure the performance of Score-CAM.

Experiment

Catch up on this link to comprehend the training, testing, and visualization pertaining to this project.

Result

Image Classification

Quantitative Result

The table below exhibits the outcome, quantitatively.

Test Metric | Score ------------ | ------------- Loss | 0.570 Accuracy | 89.79%

Accuracy and Loss Curve

acc_curve
Accuracy curves of 3D ResNet on the train and validation sets.

loss_curve
Loss curves of 3D ResNet on the train and validation sets.

Visualization

Quantitative Result

Overall DAUC and IAUC scores of the Score-CAM on the 4th layer of 3D ResNet:

Test Metric | Score ------------ | ---------------- DAUC | 0.2576 ± 0.1710 IAUC | 0.6750 ± 0.1944

Qualitative Result

The following are snapshots of the individual DAUC and IAUC scores and their Score-CAM outcomes.

bladder
The result of the bladder.

heart
The result of the heart.

kidney-left
The result of the left kidney.

kidney-left
The result of the right kidney.

lung-left
The result of the left lung.

kidney-left
The result of the right lung.

spleen
The result of the spleen.

Citation

To cite this repository:

@misc{3dviz-scorecam, title = {Visualizing 3D ResNet for Medical Image Classification With Score-CAM}, url = {https://github.com/reshalfahsi/3d-viz-score-cam}, author = {Resha Dwika Hefni Al-Fahsi}, }

Credit

Owner

  • Name: Resha Dwika Hefni Al-Fahsi
  • Login: reshalfahsi
  • Kind: user
  • Location: Yogyakarta, Indonesia

Experienced Tensorbender Strolling in the Latent Space

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "Thanks for citing this repository:"
title: "Visualizing 3D ResNet for Medical Image Classification With Score-CAM"
authors:
  - family-names: Al-Fahsi
    given-names: Resha Dwika Hefni
url: https://github.com/reshalfahsi/3d-viz-score-cam

GitHub Events

Total
Last Year

Issues and Pull Requests

Last synced: 12 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels