A deep neural network model for content-based medical image retrieval with multi-view classification

Karthik, Kannan; Kamath, S. Sowmya

doi:10.1007/s00371-020-01941-2

Cited by 37 publications

(11 citation statements)

References 31 publications

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“…The output of Otsu algorithm [35] returns a single intensity threshold that separates pixels within image into two classes, foreground and background. The dataset [26,27] is divided into the ratio of 70% and 30% images along with their associated ground truth images. The modified RESNET 50 model is trained with the segmented image features along with ground truth values.…”

Section: Proposed Workmentioning

confidence: 99%

A Framework to Detect Image Co-Saliency Using Clahe and Modified Resnet-50 Model

Mangal¹,

Garg²,

Bhatnagar³

2023

INDJCSE

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The co-saliency detection technique is being applied in numerous applications, including image query, picture annotation, extraction of image/video foreground portion, surveillance, image retrieval, in order to discover most similar and salient patterns from a set of relevant image group. We proposed a method of co-saliency detection in an image, utilizing residual neural networks (ResNet 50 model) as classification model in this study. CLAHE (contrast limited adaptive histogram equalization) and Otsu segmentation technique are employed in this paper to better isolate and detect prominent object in image as preprocessing step. The result from these technique helps the model to understand the pattern more effectively. The fine tuning of the ResNet model has been done to produce more optimized and accurate result. The network was compiled with SGDM optimizer for more optimized result. Cosal2015 and MSRC dataset. are used to train and test the model for co-salient object detection and qualitative and quantitative assessment. The dataset used is divided into 70% and 30% for training and testing purpose respectively. The proposed model outperforms in terms of F1-score and MAE score with Cosal2015 dataset and MSRC dataset when compared with the other state of art methods. The proposed work is also efficient in terms of execution time and complexity.

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Section: Proposed Workmentioning

confidence: 99%

A Framework to Detect Image Co-Saliency Using Clahe and Modified Resnet-50 Model

Mangal¹,

Garg²,

Bhatnagar³

2023

INDJCSE

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show abstract

“…DL models are also known for their ability to self generate intermediate representations, which can be more complex representations such as pictorial structure [15]. Deep learning has been applied for clinical tasks such as image retrieval and classification [16,17], disease prediction [18,19] among others.…”

Section: Related Workmentioning

confidence: 99%

Content-based medical image retrieval system for lung diseases using deep CNNs

Agrawal

Chowdhary

Agarwala

et al. 2022

Int. j. inf. tecnol.

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“…In image retrieval, case-based explainability is commonly used in scenarios such as medical image diagnosis to obtain examples of similar disease-matching images that can be compared to a case under analysis and provide additional insights to explain and support a diagnosis [3]- [5]. The retrieval process begins with a user entering an image into the retrieval system.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

et al. 2022

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Deep Learning achieves state-of-the-art results in many domains, yet its black-box nature limits its application to real-world contexts. An intuitive way to improve the interpretability of Deep Learning models is by explaining their decisions with similar cases. However, case-based explanations cannot be used in contexts where the data exposes personal identity, as they may compromise the privacy of individuals. In this work, we identify the main limitations and challenges in the anonymization of casebased explanations of image data through a survey on case-based interpretability and image anonymization methods. We empirically analyze the anonymization methods in regards to their capacity to remove personally identifiable information while preserving relevant semantic properties of the data. Through this analysis, we conclude that most privacy-preserving methods are not sufficiently good to be applied to casebased explanations. To promote research on this topic, we formalize the privacy protection of visual casebased explanations as a multi-objective problem to preserve privacy, intelligibility, and relevant explanatory evidence regarding a predictive task. We empirically verify the potential of interpretability saliency maps as qualitative evaluation tools for anonymization. Finally, we identify and propose new lines of research to guide future work in the generation of privacy-preserving case-based explanations.

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A deep neural network model for content-based medical image retrieval with multi-view classification

Cited by 37 publications

References 31 publications

A Framework to Detect Image Co-Saliency Using Clahe and Modified Resnet-50 Model

A Framework to Detect Image Co-Saliency Using Clahe and Modified Resnet-50 Model

Content-based medical image retrieval system for lung diseases using deep CNNs

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

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