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Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy
Computer Vision and Pattern Recognition · Submitted 3 Nov 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This work demonstrates a scalable approach to abnormality classification in VCE.
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The abstract, sentence by sentence
This study presents an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. Given the challenges of data imbalance, we implemented a tiered augmentation strategy using the albumentations library to enhance minority class representation. Additionally, we addressed learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and then gradually adding more specific classes based on data availability. Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity. We tested our approach using ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. This work demonstrates a scalable approach to abnormality classification in VCE.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This study presents an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. | contribution 1.00 | 0.87 | 0.06 | asserted 0.95 |
| s1 | Given the challenges of data imbalance, we implemented a tiered augmentation strategy using the albumentations library to enhance minority class representation. | method 0.99 | 0.95 | 0.03 | asserted 1.00 |
| s2 | Additionally, we addressed learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and then gradually adding more specific classes based on data availability. | method 0.98 | 0.95 | 0.06 | asserted 1.00 |
| s3 | Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity. | method 0.98 | 0.92 | 0.05 | asserted 1.00 |
| s4 | We tested our approach using ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. | method 1.00 | 0.95 | 0.04 | asserted 0.99 |
| s5 | This work demonstrates a scalable approach to abnormality classification in VCE. | interpretation 0.89 | 0.76 | 0.78 | asserted 1.00 |
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The paper
Original abstract
This study presents an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. Given the challenges of data imbalance, we implemented a tiered augmentation strategy using the albumentations library to enhance minority class representation. Additionally, we addressed learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and then gradually adding more specific classes based on data availability. Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity. We tested our approach using ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. This work demonstrates a scalable approach to abnormality classification in VCE.
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What this paper does with the arXiv papers it cites
What it cites · 2 sentences without a confident label
Uses the method or tool of 4 papers
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The hybrid architecture consists of a ViT branch [ref] and a ResNet34 branch using which features are extracted.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale · Final Pipeline and Implementation · 0.98
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Experiments with both ResNet50 [ref] and a custom ViT-CNN hybrid architecture were conducted.
Deep Residual Learning for Image Recognition · Introduction · 0.94
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Since we are using pretrained models with fine tuning, we have implemented a post-hoc technique called Gradient-weighted Class Activation Mapping (GradCAM) [ref] .
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization · Explainabiltiy · 0.99
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