Summary of Capsule Vision Challenge 2024: Multi-class Abnormality Classification For Video Capsule Endoscopy, by Aakarsh Bansal et al.
Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy
by Aakarsh Bansal, Bhuvanesh Singla, Raajan Rajesh Wankhade, Nagamma Patil
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study develops a model for classifying abnormalities in video capsule endoscopy (VCE) frames. To address data imbalance challenges, a tiered augmentation strategy using albumentations is implemented to enhance minority class representation. The approach also addresses learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and gradually adding more specific classes based on data availability. A flexible architecture in PyTorch enables seamless adjustments to classification complexity. The study uses ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to look at video capsule endoscopy (VCE) frames to find problems. To make it work better, they used special techniques to help the computer see more abnormal images. They also made the computer learn in steps, so it can get better and better at finding different types of problems. The team developed this method using a powerful tool called PyTorch and tested it with two special models: ResNet50 and a custom ViT-CNN hybrid model. |
Keywords
» Artificial intelligence » Classification » Cnn » Vit
