A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation

Wan, Yuchai; Zheng, Zhongshu; Liu, Ran; Zhu, Zheng; Zhou, Hongen; Zhang, Xun; Boumaraf, Said

doi:10.3390/life11060582

Cited by 7 publications

(3 citation statements)

References 51 publications

(57 reference statements)

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“…Wan et al [18] presented the MMF-CNN, a novel methodology that integrates multi-level and multi-scale fusion techniques within Convolutional Neural Networks (CNNs) for the purpose of detecting liver lesions in magnetic resonance imaging (MRI). They systematically used their inventive technique on several cutting-edge deep learning systems.…”

Section: Role Of Ai In Medicinementioning

confidence: 99%

Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images

Kavitha,

Hussain,

Chillakuru

et al. 2024

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Plagued by a high incidence rate worldwide, liver cancer comes in sixth place among all cancers. The degree of differentiation, which may be roughly divided into three types: weakly moderately differentiated, highly differentiated, and differentiated, has a substantial impact on the malignancy level of this terrible illness. In direction to improve the existence of affected role and life expectancy, therapeutic techniques that are customised for these varied levels of diversity are essential. The gold standard for identifying the main liver cancer, hepatocellular carcinoma (HCC), is a histopathological picture that allows for exact distinction of liver tumours at different stages of development. This study explores the creative use of the R-CNN algorithm for deep learning for the astute categorization of histological pictures associated with liver cancer that undergoes differentiation. So this study compared how well R-CNN did compared to five other popular deep learning models -SKNet, ResNet CBAM, ResNet50, VGG16, and SENet. It was really important to set up a good system to collect a lot of different data for this project. This would make sure they had enough info to properly test how well the different math models worked. They also created a thorough and precise method using things like recall, confusion matrices, F1-scores, and accuracy to analyze how the models performed. The results showed that R-CNN did amazingly well, with an accuracy of 96.7%! That means it was able to classify things correctly almost all the time. This demonstrates it had the most accurate predictions out of everything they looked at. Additionally, the R-CNN model proved to be very reliable and able to generalize well. In other words, it should work just as good on new, unseen data as it did on the information it was originally skilled on. This study compares the routine of R-CNN to five other well-established deep learning models: SKNet, ResNet CBAM, ResNet50, VGG16, and SENet. Developing a robust data collection infrastructure is critical to enable the project to ensure a large and varied dataset for thorough evaluation of the mathematical models under consideration. A comprehensive and precise evaluation approach was provided through judicious usage of metrics like F1-Score, recall, confusion matrix, and accuracy to analyze the model performance. Testing results demonstrate R-CNN's capabilities, evidenced by its notable 96. 7% accuracy indicating highly precise classification outcomes. Furthermore, the model exhibits strong reliability and generalizability.

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Section: Role Of Ai In Medicinementioning

confidence: 99%

Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images

Kavitha,

Hussain,

Chillakuru

et al. 2024

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“…In short, they extract the influence of a feature on the output for a given sample. They are based on the gradients of the learned model and, in [88], have been used to provide visual MRI explanations of liver lesions. For small datasets, it is even possible to include some kind of medical knowledge as structural constraint rules over the attention maps during the process design [89].…”

Section: Interpretation Of Neural Network Architecturesmentioning

confidence: 99%

A Scoping Review on the Progress, Applicability, and Future of Explainable Artificial Intelligence in Medicine

González-Alday,

García-Cuesta,

Kulikowski

et al. 2023

Applied Sciences

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Due to the success of artificial intelligence (AI) applications in the medical field over the past decade, concerns about the explainability of these systems have increased. The reliability requirements of black-box algorithms for making decisions affecting patients pose a challenge even beyond their accuracy. Recent advances in AI increasingly emphasize the necessity of integrating explainability into these systems. While most traditional AI methods and expert systems are inherently interpretable, the recent literature has focused primarily on explainability techniques for more complex models such as deep learning. This scoping review critically analyzes the existing literature regarding the explainability and interpretability of AI methods within the clinical domain. It offers a comprehensive overview of past and current research trends with the objective of identifying limitations that hinder the advancement of Explainable Artificial Intelligence (XAI) in the field of medicine. Such constraints encompass the diverse requirements of key stakeholders, including clinicians, patients, and developers, as well as cognitive barriers to knowledge acquisition, the absence of standardised evaluation criteria, the potential for mistaking explanations for causal relationships, and the apparent trade-off between model accuracy and interpretability. Furthermore, this review discusses possible research directions aimed at surmounting these challenges. These include alternative approaches to leveraging medical expertise to enhance interpretability within clinical settings, such as data fusion techniques and interdisciplinary assessments throughout the development process, emphasizing the relevance of taking into account the needs of final users to design trustable explainability methods.

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“…They apply proposed approach to various state-of-the-art deep learning architectures. The experimental results demonstrate the effectiveness of their approach [ 39 ]. Zhou et al introduced basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their use in liver disease medical imaging Clinical applications in the field, such as detecting and evaluating focal liver lesions, promoting treatment and predicting liver response to treatment.…”

Section: Introductionmentioning

confidence: 98%

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

Chen

et al. 2022

BMC Med Inform Decis Mak

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Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

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A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation

Cited by 7 publications

References 51 publications

Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images

Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images

A Scoping Review on the Progress, Applicability, and Future of Explainable Artificial Intelligence in Medicine

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

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