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A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and

Sadia Munawar¹, Fareeha Hanif^2,3, Ali Raza^4,5,6

¹Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, Pakistan.

Scientific Reports

|June 10, 2026

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This study introduces a novel framework using Contrastive Language Image Pretraining (CLIP) for accurate lung cancer histopathology classification. The method effectively distinguishes between benign tissue, adenocarcinoma, and squamous cell carcinoma, achieving high accuracy.

Area of Science:

Oncology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Accurate lung cancer histopathological classification is critical for patient diagnosis and treatment.
Existing methods may face challenges in distinguishing subtle differences between lung cancer subtypes.
Automated classification systems can enhance efficiency and consistency in pathology workflows.

Purpose of the Study:

To develop and evaluate a Contrastive Language Image Pretraining (CLIP)-based framework for multiclass lung histopathology classification.
To differentiate between benign lung tissue, lung adenocarcinoma, and lung squamous cell carcinoma using a multimodal approach.
To assess the performance and robustness of the proposed CLIP framework in automated lung cancer diagnosis.

Main Methods:

Keywords:

CLIP Contrastive learning Deep learning Digital pathology Focal Loss Histopathology image classification Lung cancer Multiclass classification Prompt engineering Vision language model

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Utilized a pretrained CLIP ViT-B/32 backbone for feature extraction.
Employed domain-specific prompt engineering and multimodal image-text pairing.
Implemented similarity-based classification within a shared embedding space.
Incorporated data augmentation, Focal Loss, AdamW optimization, and learning rate scheduling for robust fine-tuning.

Main Results:

Achieved a best validation accuracy of 95.20% on lung histopathology classification.
Obtained a macro Area Under the Curve (AUC) of 0.9870 and a micro AUC of 0.9877.
Demonstrated steady performance improvement across training epochs, with early stopping at epoch 23.

Conclusions:

Integrating CLIP with pathology-specific text prompts offers a reliable framework for automated lung cancer histopathology classification.
The proposed method shows significant potential for advancing intelligent digital pathology systems.
This approach can aid in timely diagnosis and treatment planning for lung cancer patients.