Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods

Malafaia, Mafalda; Silva, Francisco; Neves, Inês; Pereira, Tânia; Oliveira, Hélder P.

doi:10.1109/access.2022.3214824

Cited by 9 publications

(2 citation statements)

References 30 publications

(38 reference statements)

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“…The black box characteristic of the deep learning method itself and the lack of tools to examine the behavior of the black box model are the main reasons hindering its application in the medical field [527]. Images of different cancers vary widely and, while significant research advances have been made in explainable deep-learning cancer detection systems, most of the methods used in the literature are local and post-hoc approaches [528][529][530] and usually explainable models or methods for specific cancer image analysis [298], which are still far from the clinical requirements for the formation of explainable imaging markers. Explainable methods of deep learning models will be a research focus in cancer diagnosis, in addition to the need to include clinicians in the design process of these algorithms and the need to build inherently explainable deep learning models for cancer detection [531].…”

Section: Model Explainabilitymentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

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Section: Model Explainabilitymentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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“…Based on the CNN model, explainable artificial intelligence (XAI) is a useful tool in the medical industry that increases the transparency of automatically generated prediction models. It speeds up the creation of predictive models, utilizing expertise in the field and helping to produce results that are understandable to humans [14,15]. There are several ways to show the most active areas and to make a model more explicable.…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration

Hasan,

Haque,

Sabuj

et al. 2024

Technologies

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To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.

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Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition

Finzel,

Rieger,

Kuhn

et al. 2023

Lecture Notes in Computer Science

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Research in the field of explainable artificial intelligence has produced a vast amount of visual explanation methods for deep learning-based image classification in various domains of application. However, there is still a lack of domain-specific evaluation methods to assess an explanation’s quality and a classifier’s performance with respect to domain-specific requirements. In particular, evaluation methods could benefit from integrating human expertise into quality criteria and metrics. Such domain-specific evaluation methods can help to assess the robustness of deep learning models more precisely. In this paper, we present an approach for domain-specific evaluation of visual explanation methods in order to enhance the transparency of deep learning models and estimate their robustness accordingly. As an example use case, we apply our framework to facial expression recognition. We can show that the domain-specific evaluation is especially beneficial for challenging use cases such as facial expression recognition and provides application-grounded quality criteria that are not covered by standard evaluation methods. Our comparison of the domain-specific evaluation method with standard approaches thus shows that the quality of the expert knowledge is of great importance for assessing a model’s performance precisely.

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Robustness Analysis of Deep Learning-Based Lung Cancer Classification Using Explainable Methods

Cited by 9 publications

References 30 publications

Deep Learning for Medical Image-Based Cancer Diagnosis

Deep Learning for Medical Image-Based Cancer Diagnosis

An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration

Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition

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