Skip to content

Latest commit

 

History

History
91 lines (59 loc) · 4.92 KB

README.md

File metadata and controls

91 lines (59 loc) · 4.92 KB

A Balancing Act: Optimizing Classification and Retrieval in Cross-Modal Vision Models

This repository includes the implementation of a simple, yet effective method to balance classification and contrastive objectives in general computer vision and computational Pathology. The paper is currently under review at MIDL 2025.

Abstract

Despite the promising capabilities of vision-language models (VLMs) across diverse tasks, recent studies reveal that they struggle with the fundamental task of image classification. In this study, we explore leveraging state-of-the-art task-specific classification models as a foundation for VLMs, aiming to preserve strong classification performance. Specifically, we assess the impact of contrastive tuning to enable cross-modal retrieval capabilities on a Hierarchical Image Pyramid Transformer (HIPT) trained for prostate cancer grading in Whole-Slide Images (WSIs) and a ViT-Base model trained for multi-label classification on natural images. Our results demonstrate that contrastive fine-tuning creates a clear trade-off: classification accuracy rapidly deteriorates toward zero as vision-text alignment improves. By balancing the two objectives in the loss function during fine-tuning, we achieve competitive slide-level retrieval performance while maintaining classification accuracy.

Requirements

  • python 3.9+
  • install requirements via pip3 install -r requirements.txt

Code

[Optional] Configure wandb

If you want to benefit from wandb logging, you need to follow these simple steps:

  • grab your wandb API key under your wandb profile
  • run the following command in your terminal: export WANDB_API_KEY=<your_personal_key>
  • update wandb parameters in the relevant .yaml config files (see next)

COCO experiments

1.Download COCO 2014 dataset Download the 2014 Train/Val images + annotations with the below links and store it under data/

wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip

2.[Optional] Create folds

Create new folds from the COCO dataset:

python3 src/coco/preprocess.py

If you want to replicate my experiments, you can skip this step and use the folds saved under data/folds/

2. Adapt config file

Create a .yaml configuration file under config/. You can take inspiration from existing files coco.yaml for the coco experiments and medical.yaml for the medical experiments

3. Contrastive Tuning using single $\lambda$ on a single fold

python3 src/run.py --config-name {config_file_name}.yaml

Alternatively, you can adjust paramters dynamically e.g. in a bash script:

python3 src/run.py --config-name coco data.fold=$fold lambda_param=$lambda data.train_path="./data/folds/train_fold$fold.json"

Prostate cancer grading experiments

🚧 Under Construction 🚧

Prerequisite

We pretrained a Local H-ViT model, leveraging a frozen patch-level Transformer, while finetuning region-level and slide-level Transformers for multi-class ISUP Grade classification. For implementation details, please refer to the HIPT implementation. Train a single fold on the extracted features using the configuration in config/hipt_train_panda.yaml. The final model achieves state-of-the-art performance on the PANDA test set with a quadratic kappa score of $0.892$.

To use this model during contrastive tuning, you need to:

  1. Patch Extraction:

    Extract $2048×2048$ patches from whole-slide images (WSIs) using HS2P. Patches are sampled at $0.5$ pixel spacing to capture relevant tissue regions.

  2. Feature Extraction: Use a pretrained model to extract local features for each WSI, stored as tensors of shape (M, 64, 384). (See HIPT for details).

  3. Adapt config file:
    Download the pretrained HIPT model weights (LocalGlobalHIPT_2048_768.pt) and update the config/medical.yaml. Set vision.model_weights to the path where the weights are strored and vision.local_features_dir to the output directory for feature extraction. This enables the model to generate 768-dim slide-level embeddings for prostate slides.

Run Contrastive Tuning using $\lambda$ on a single fold:

python3 src/run.py --config-name medical

References

[1] C. Grisi, "Hierarchical Image Pyramid Transformer", Available at: [https://github.com/clemsgrs/hipt]https://github.com/clemsgrs/hipt)

[2] C. Grisi, "HS2P: Histopathology Slide Pre-processing Pipeline", Available at: https://github.com/clemsgrs/hs2p