CheXpert-5-convnextv2-tiny-384 Open Source Model - Free Deployment to Assist Chest X-ray Image Classification

Chexpert 5 Convnextv2 Tiny 384

Developed by shreydan

This model is a fine-tuned version of facebook/convnextv2-tiny-22k-384 on the CheXpert dataset, primarily used for chest X-ray image classification tasks.

Image Classification

Transformers

Open Source License:Apache-2.0 #Chest X-ray Analysis #Multi-pathology Detection #Medical Imaging Diagnosis

Downloads 66

Release Time : 5/21/2024

Model Overview

The model specializes in chest X-ray image classification, capable of identifying five common thoracic abnormalities: atelectasis, cardiomegaly, consolidation, edema, and pleural effusion.

Model Features

High-performance Medical Image Analysis

Excels in identifying five common thoracic abnormalities with AUROC metrics all exceeding 0.79

Based on ConvNeXtV2 Architecture

Utilizes the advanced ConvNeXtV2-tiny architecture, pre-trained on ImageNet-22K

Fine-tuned on Professional Medical Dataset

Fine-tuned using the CheXpert professional chest X-ray dataset to adapt to medical imaging characteristics

Model Capabilities

Chest X-ray Classification

Medical Image Analysis

Atelectasis Detection

Cardiomegaly Detection

Consolidation Detection

Edema Detection

Pleural Effusion Detection

Use Cases

Medical Diagnosis Assistance

Thoracic Abnormality Screening

Assists radiologists in screening for common abnormalities in chest X-rays

Achieves AUROC of 0.79-0.93 for five abnormality detections

Emergency Triage

Rapidly identifies thoracic abnormalities in emergency patients to aid triage decisions

🚀 CheXpert-5-convnextv2-tiny-384

This model is a fine - tuned version of facebook/convnextv2-tiny-22k-384 on the CheXpert dataset, achieving specific results on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of facebook/convnextv2-tiny-22k-384 on the CheXpert dataset. It achieves the following results on the evaluation set:

Loss: 0.1009
Auroc Atelectasis: 0.7943
Auroc Cardiomegaly: 0.8187
Auroc Consolidation: 0.9269
Auroc Edema: 0.9233
Auroc Pleural effusion: 0.9315
Specificity Atelectasis: 0.7891
Specificity Cardiomegaly: 1.0
Specificity Consolidation: 0.9948
Specificity Edema: 0.8407
Specificity Pleural effusion: 0.8038
Exact Match: 0.4464
Hamming Distance: 0.1804

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 2500
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Auroc Atelectasis	Auroc Cardiomegaly	Auroc Consolidation	Auroc Edema	Auroc Pleural effusion	Specificity Atelectasis	Specificity Cardiomegaly	Specificity Consolidation	Specificity Edema	Specificity Pleural effusion	Exact Match	Hamming Distance
0.0891	1.0	6120	0.0893	0.7323	0.8366	0.7020	0.8387	0.8702	0.4510	0.9661	1.0	0.6596	0.5392	0.2616	0.2444
0.0854	2.0	12240	0.0831	0.7535	0.8556	0.7350	0.8651	0.8881	0.7293	0.9042	0.9936	0.7083	0.6259	0.3571	0.1973
0.082	3.0	18360	0.0824	0.7683	0.8696	0.7473	0.8720	0.8961	0.6956	0.8196	0.9881	0.6087	0.6611	0.3298	0.2177
0.0799	4.0	24480	0.0802	0.7749	0.8720	0.7562	0.8783	0.9005	0.7450	0.8831	0.9608	0.7341	0.6984	0.3802	0.1880
0.0759	5.0	30600	0.0793	0.7795	0.8746	0.7583	0.8818	0.9030	0.7277	0.8948	0.9711	0.7618	0.7045	0.3869	0.1823
0.0739	6.0	36720	0.0798	0.7787	0.8727	0.7561	0.8812	0.9031	0.7461	0.8921	0.9690	0.7487	0.7074	0.3886	0.1824

Framework versions

Transformers 4.41.0
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1

📄 License

The model is licensed under the Apache 2.0 license.

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