Dynamic distance learning for joint assessment of visual and semantic similarities within the framework of medical image retrieval

Baâzaoui, Abir; Abderrahim, Marwa; Barhoumi, Walid

doi:10.1016/j.compbiomed.2020.103833

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Content-based medical image retrieval (CBMIR) is a recent DL-based methodology that allows pathologists a quick and precise search in previously diagnosed and treated cases [9]. In CBMIR, image features, such as texture, shape, color, and intensity, are extracted from the query and data set; then, a similarity measure is applied to compare the query features with the features of the database [10]. The retrieved images are ranked according to their similarity to the query image, and the most relevant images are displayed to the user.…”

Section: Introductionmentioning

confidence: 99%

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

Tabatabaei,

Wang,

Colomer

et al. 2023

Bioengineering

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The paper proposes a federated content-based medical image retrieval (FedCBMIR) tool that utilizes federated learning (FL) to address the challenges of acquiring a diverse medical data set for training CBMIR models. CBMIR is a tool to find the most similar cases in the data set to assist pathologists. Training such a tool necessitates a pool of whole-slide images (WSIs) to train the feature extractor (FE) to extract an optimal embedding vector. The strict regulations surrounding data sharing in hospitals makes it difficult to collect a rich data set. FedCBMIR distributes an unsupervised FE to collaborative centers for training without sharing the data set, resulting in shorter training times and higher performance. FedCBMIR was evaluated by mimicking two experiments, including two clients with two different breast cancer data sets, namely BreaKHis and Camelyon17 (CAM17), and four clients with the BreaKHis data set at four different magnifications. FedCBMIR increases the F1 score (F1S) of each client from 96% to 98.1% in CAM17 and from 95% to 98.4% in BreaKHis, with 11.44 fewer hours in training time. FedCBMIR provides 98%, 96%, 94%, and 97% F1S in the BreaKHis experiment with a generalized model and accomplishes this in 25.53 fewer hours of training.

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Section: Introductionmentioning

confidence: 99%

WWFedCBMIR: World-Wide Federated Content-Based Medical Image Retrieval

Tabatabaei,

Wang,

Colomer

et al. 2023

Bioengineering

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“…Another complexity in the multimedia retrieval process is users’ intention. In most of the existing methods, the retrieval process is often restricted to a similar category of multimedia content like images [ 16 ]. It minimizes the Quality of Service (QoS) of multimedia retrieval due to the failure in identifying the intention of users' search owing to the lack of type diversity [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A new design of multimedia big data retrieval enabled by deep feature learning and Adaptive Semantic Similarity Function

Sujatha

Subramaniam²,

Robin³

2022

Multimedia Systems

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Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches. Multimedia big data consists of the most significant features with different types of data. Even though the multimedia supports various data formats with corresponding storage frameworks, similar semantic information is expressed by the multimedia. The overlap of semantic features is most efficient for theory and research related to semantic memory. Correspondingly, in recent years, deep multimodal hashing gets more attention owing to the efficient performance of huge-scale multimedia retrieval applications. On the other hand, the deep multimodal hashing has limited efforts for exploring the complex multilevel semantic structure. The main intention of this proposal is to develop enhanced deep multimedia big data retrieval with the Adaptive Semantic Similarity Function (A-SSF). The proposed model of this research covers several phases “(a) Data collection, (b) deep feature extraction, (c) semantic feature selection and (d) adaptive similarity function for retrieval. The two main processes of multimedia big data retrieval are training and testing. Once after collecting the dataset involved with video, text, images, and audio, the training phase starts. Here, the deep semantic feature extraction is performed by the Convolutional Neural Network (CNN), which is again subjected to the semantic feature selection process by the new hybrid algorithm termed Spider Monkey-Deer Hunting Optimization Algorithm (SM-DHOA). The final optimal semantic features are stored in the feature library. During testing, selected semantic features are added to the map-reduce framework in the Hadoop environment for handling the big data, thus ensuring the proper big data distribution. Here, the main contribution termed A-SSF is introduced to compute the correlation between the multimedia semantics of the testing data and training data, thus retrieving the data with minimum similarity. Extensive experiments on benchmark multimodal datasets demonstrate that the proposed method can outperform the state-of-the-art performance for all types of data.

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