thaiphonghuan/BLIP-finetuned-chest-xray-v1

Fine-tuned BLIP on Chest X-rays (Indiana University)

This repository contains a fine-tuned BLIP (Bootstrapped Language-Image Pretraining) model trained on the Chest X-rays (Indiana University) dataset. The model is adapted for vision–language tasks in the medical imaging domain, particularly chest X-ray understanding.

🧠 Model Description

• —Base model: BLIP (Bootstrapped Language-Image Pretraining)
• —Fine-tuning domain: Medical imaging
• —Modality: Vision–Language (Image + Text)
• —Target data: Chest X-ray images (frontal & lateral views)

The goal of fine-tuning is to adapt BLIP to better capture radiological visual patterns and associated semantic information from chest X-ray images.

📊 Dataset Information

The model is fine-tuned using the Chest X-rays (Indiana University) dataset.

Dataset Source

• —Kaggle: https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university

Image Preprocessing Pipeline

Original images were provided in raw DICOM format. Each image was converted to PNG with the following preprocessing steps:

1. 1.Outlier clipping
2. 2.The top and bottom 0.5% of DICOM pixel values were clipped
3. 3.Purpose: eliminate extremely dark or bright pixel outliers

1. 1.Intensity normalization
2. 2.DICOM pixel values were linearly scaled to the 0–255 range

1. 1.Resizing
2. 2.Images were resized so that the shorter side is 2048 pixels
3. 3.This was done to comply with Kaggle dataset size limits

1. 1.View classification
2. 2.Each image was manually classified into:
3. 3.Frontal chest X-ray
4. 4.Lateral chest X-ray