Dictionary Pruning with Visual Word Significance for Medical Image Retrieval

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches.

Creating Visual Vocabularies for The Retrieval And Classification of Histopathology Images

State-of-the-art technologies in the fields of computer vision and machine learning led the automatic recognition of malignant structures in histopathology images. More than often, such structures are reported to be found in glands, where different morphological characteristics indicate the existence of a variety of adenocarcinomas, including prostate, breast, lung and colon cancer. Classification of images containing glandular representations in different cancer types can be performed in the whole image by the utilization of a combination of local and global features. The proposed methodology involves the exploitation of a notion utilized often in text mining called Bag of Words and employed in the service of Computer Vision with the name of Bag of Visual Words (BOVW) for the development of a retrieval and classification system for pathology images. The paper discusses the technical details of implementation, the enhancement of the BOVW technique, while some initial results using public datasets are presented.

Content based medical image retrieval using topic and location model

BACKGROUND AND OBJECTIVE

Retrieval of medical images from an anatomically diverse dataset is a challenging task. Objective of our present study is to analyse the automated medical image retrieval system incorporating topic and location probabilities to enhance the performance.

MATERIALS AND METHODS

In this paper, we present an automated medical image retrieval system using Topic and Location Model. The topic information is generated using Guided Latent Dirichlet Allocation (GuidedLDA) method. A novel Location Model is proposed to incorporate the spatial information of visual words. We also introduce a new metric called position weighted Precision (wPrecision) to measure the rank order of the retrieved images.

RESULTS

Experiments on two large medical image datasets - IRMA 2009 and Multimodal dataset - revealed that the proposed method outperforms existing medical image retrieval systems in terms of Precision and Mean Average Precision. The proposed method achieved better Mean Average Precision (86.74%) compared to the recent medical image retrieval systems using the Multimodal dataset with 7200 images. The proposed system achieved better Precision (97.5%) for top ten images compared to the recent medical image retrieval systems using IRMA 2009 dataset with 14,410 images.

CONCLUSION

Supplementing spatial details of visual words to the Topic Model enhances the retrieval efficiency of medical images from large repositories. Such automated medical image retrieval systems can be used to assist physician to retrieve medical images with better precision compared to the state-of-the-art retrieval systems.

An Automatic Classification Method on Chronic Venous Insufficiency Images

Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.