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Classification and Quantification of ILD patterns(CLIPQ)

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Welcome to our application for classifying and quantifying interstitial lung disease(ILD) patterns. Our focus is on four key patterns: Ground Glass Opacity, Fibrosis, Honeycombing, and Nodules.

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Disclaimer:This tool is intended for research purposes only. It is not designed, validated, or approved for clinical diagnosis, treatment, or patient care. Users are solely responsible for interpreting the outputs and ensuring compliance with ethical guidelines, institutional regulations, and applicable laws. The developers and providers of this tool assume no liability for any consequences resulting from its use. If you require medical assistance, please consult a qualified healthcare professional.
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Class Distribution Per Slice

× Expanded Piechart

HRCT Slice Display

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Segmented Lung Parenchyma

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Grad-CAM Results

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Quantification Results

Fibrosis
Nodules
Ground Glass
Honeycombing
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About Us

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About Us

Welcome to our AI-driven medical imaging tool for Interstitial Lung Disease analysis.

Our application leverages deep learning to assist in lung disease segmentation, classification, and quantification.

Developed by researchers and AI experts, we aim to support radiologists and medical professionals with advanced image analysis.