A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Uyulan, Çağlar; Ergüzel, Türker Tekin; Türk, Ömer; Farhad, Shams; Metin, Barış; Tarhan, Nevzat

doi:10.1177/15500594221122699

Cited by 7 publications

(6 citation statements)

References 32 publications

(41 reference statements)

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“…In image-based drug discovery, Class Activation Mapping (CAM) plays a pivotal role in localizing critical features within input data. Specifically tailored for deep learning models, CAM visually identifies regions crucial for a specific class prediction [22]. This localization not only aids in validating model predictions but also contributes to the broader interpretability of intricate neural network architectures applied to drug-related image data.…”

Section: B Drug Discovery Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

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Section: B Drug Discovery Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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“…The use of XAI techniques has increased dramatically as a result of COVID-19 detection [30]. Generally speaking, these techniques may be distinguished by either using the entire CT scan or only the lung segmentation for COVID-19 identification.…”

Section: Literature On DL Models' Explainability In Medical Imagingmentioning

confidence: 99%

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

Nhlapho,

Atemkeng,

Brima

et al. 2024

Information

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The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making mechanisms, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging ten state-of-the-art DL frameworks with transfer learning, we conducted a comprehensive evaluation encompassing both classification accuracy and interpretability. These models underwent thorough training, testing, and fine-tuning, resulting in EfficientNetB0, DenseNet121, and Xception outperforming the other models. These top-performing models were examined using adaptive path-based techniques to understand the underlying decision-making mechanisms. Grad-CAM and Grad-CAM++ highlighted critical image regions where the models identified patterns and features associated with each class of the brain tumor. The regions where the models identified patterns and features correspond visually to the regions where the tumors are located in the images. This result shows that DL models learn important features and patterns in the regions where tumors are located for decision-making.

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“…However, it should be noted that not all pretrained models are instantly compatible with fMRI data, and some model tweaking or data reduction may be required. Nevertheless, pretrained models are great assistive tools for streamlining DL pipelines and classifying neurological conditions (for example, see Meng et al., 2022 ; Ramzan et al., 2019 ; Uyulan et al., 2023 ). See Table 2 for more information on specific pretrained neural networks.…”

Section: A Guide To Assistive Tools For Fmri and Deep Learning Pipelinesmentioning

confidence: 99%

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example

Warren,

Khan,

Moustafa

2024

Brain and Behavior

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BackgroundDeep‐learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain‐specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real‐world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders.Objectives and MethodsIn this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.ResultsIn turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.ConclusionsWe are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders.

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A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Cited by 7 publications

References 32 publications

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example

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