Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval

Medical image retrieval refers to the task of finding similar images for given query images in a database, with applications such as diagnosis support, treatment planning, and educational tools for inexperienced medical practitioners. While traditional medical image retrieval was performed using clinical metadata, content-based medical image retrieval (CBMIR) relies on the characteristic features of the images, such as color, texture, shape, and spatial features. Many approaches have been proposed for CBMIR, and among them, using pre-trained convolutional neural networks (CNNs) is a widely utilized approach. However, considering the recent advances in the development of foundation models for various computer vision tasks, their application for CBMIR can be also investigated for its potentially superior performance. In this study, we used several pre-trained feature extractors from well-known pre-trained CNNs (VGG19, ResNet-50, DenseNet121, and EfficientNetV2M) and pre-trained foundation models (MedCLIP, BioMedCLIP, OpenCLIP, CONCH and UNI) and investigated the CBMIR performance on a subset of the MedMNIST V2 dataset, including eight types of 2D and 3D medical images. Furthermore, we also investigated the effect of image size on the CBMIR performance. Our results show that, overall, for the 2D datasets, foundation models deliver superior performance by a large margin compared to CNNs, with UNI providing the best overall performance across all datasets and image sizes. For 3D datasets, CNNs and foundation models deliver more competitive performance, with CONCH achieving the best overall performance. Moreover, our findings confirm that while using larger image sizes (especially for 2D datasets) yields slightly better performance, competitive CBMIR performance can still be achieved even with smaller image sizes. Our codes to generate and reproduce the results are available on GitHub.

Breast Histopathology Image Retrieval by Attention-based Adversarially Regularized Variational Graph Autoencoder with Contrastive Learning-Based Feature Extraction

Breast cancer is a significant global health concern, particularly for women. Early detection and appropriate treatment are crucial in mitigating its impact, with histopathology examinations playing a vital role in swift diagnosis. However, these examinations often require a substantial workforce and experienced medical experts for proper recognition and cancer grading. Automated image retrieval systems have the potential to assist pathologists in identifying cancerous tissues, thereby accelerating the diagnostic process. Nevertheless, due to considerable variability among the tissue and cell patterns in histological images, proposing an accurate image retrieval model is very challenging. This work introduces a novel attention-based adversarially regularized variational graph autoencoder model for breast histological image retrieval. Additionally, we incorporated cluster-guided contrastive learning as the graph feature extractor to boost the retrieval performance. We evaluated the proposed model's performance on two publicly available datasets of breast cancer histological images and achieved superior or very competitive retrieval performance, with average mAP scores of 96.5% for the BreakHis dataset and 94.7% for the BACH dataset, and mVP scores of 91.9% and 91.3%, respectively. Our proposed retrieval model has the potential to be used in clinical settings to enhance diagnostic performance and ultimately benefit patients.