Stage 1 - MAE Pre-training: Self-supervised learning on unlabeled chest X-rays (70 epochs)
Stage 2 - Fine-tuning ViT: Supervised fine-tuning for multi-label classification (20 epochs)

🏥 Dataset

Dataset: tta1301/nih-chest-xray-small

Statistic	Value
Total images	>10,000
Disease classes	15 (14 diseases + No Finding)
Train/Val/Test split	70/15/15
Image size	224x224

Disease Classes (15 classes)

Index	Disease (English)	Disease (Vietnamese)
0	No Finding	Không phát hiện bất thường
1	Atelectasis	Xẹp phổi
2	Cardiomegaly	Tim to
3	Effusion	Tràn dịch màng phổi
4	Infiltration	Thâm nhiễm phổi
5	Mass	Khối u phổi
6	Nodule	Nốt phổi
7	Pneumonia	Viêm phổi
8	Pneumothorax	Tràn khí màng phổi
9	Consolidation	Đông đặc phổi
10	Edema	Phù phổi
11	Emphysema	Khí phế thũng
12	Fibrosis	Xơ phổi
13	Pleural_Thickening	Dày màng phổi
14	Hernia	Thoát vị hoành

🚀 Training Results

Stage 1 - MAE Pre-training (70 epochs)

Epoch	Loss
0	0.7709
20	0.3218
40	0.1987
69	0.1168

Stage 2 - Fine-tuning (20 epochs)

Metric	Train	Validation	Test
Accuracy	0.9306	0.9307	0.9025
Micro F1	0.9254	0.9213	0.8932
Macro F1	0.8912	0.8876	0.8567
ROC-AUC	0.9789	0.9754	0.9612

Per-class F1 Score (Test)

Disease	F1
No Finding	0.95
Hernia	0.945
Pneumothorax	0.912
Cardiomegaly	0.903
Edema	0.894
Pneumonia	0.892
Effusion	0.885
Mass	0.876
Consolidation	0.873
Atelectasis	0.859
Emphysema	0.854
Nodule	0.843
Fibrosis	0.833
Pleural_Thickening	0.823
Infiltration	0.812

💻 Usage

python

from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

# Load model
processor = AutoImageProcessor.from_pretrained("tta1301/xray-vit-classifier-v3")
model = AutoModelForImageClassification.from_pretrained("tta1301/xray-vit-classifier-v3")
model.eval()

# Disease labels (updated order with No Finding)
DISEASES = [
    'No Finding',           # 0
    'Atelectasis',          # 1
    'Cardiomegaly',         # 2
    'Effusion',             # 3
    'Infiltration',         # 4
    'Mass',                 # 5
    'Nodule',               # 6
    'Pneumonia',            # 7
    'Pneumothorax',         # 8
    'Consolidation',        # 9
    'Edema',                # 10
    'Emphysema',            # 11
    'Fibrosis',             # 12
    'Pleural_Thickening',   # 13
    'Hernia'                # 14
]

def predict_chest_xray(image_path, threshold=0.3):
    image = Image.open(image_path).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.sigmoid(outputs.logits)[0]
    
    results = {DISEASES[i]: float(probs[i]) 
               for i in range(len(DISEASES)) if probs[i] > threshold}
    return dict(sorted(results.items(), key=lambda x: x[1], reverse=True))

# Example
result = predict_chest_xray("chest_xray.jpg")
print(result)