Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Laxmi Yadav, Girish Chandra, Divakar Yadav
DOI Link: https://doi.org/10.22214/ijraset.2025.71641
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Vision Transformers (ViTs) and Explainable AI (XAI) are revolutionizing breast cancer detection and classification. ViTs have proven to perform better than conventional Convolutional Neural Networks. XAI techniques are important for enhancing the transparency and trustworthiness of the models, making them more acceptable to healthcare professionals and patients.Current research is exploring self-attention mechanisms within ViTs to generate inherent explanations and using XAI to identify and mitigate biases in models. These advancements are being applied to tasks such as identifying breast cancer subtypes and predicting treatment response. To increase efficiency and speed up training, transfer learning—in which models are pre-trained on huge datasets before being modified for particular breast cancer tasks—is also becoming more common. The lack of huge, high-quality datasets and the high computational expenses of training ViTs are two issues that persist despite advancements. Future studies will concentrate on building more dependable XAI methods, larger and more varied datasets, and more effective ViT designs. User-friendly XAI tools are needed for clinical workflows. Addressing concerns about bias, fairness, and transparency is essential for responsible AI use in healthcare. Data standardization is also needed to ensure consistent results across different locations.
Breast cancer (BC) diagnosis benefits from advanced, accurate imaging techniques, but traditional methods have limitations in sensitivity and specificity. Deep learning, especially convolutional neural networks (CNNs), has improved BC image analysis, yet CNNs struggle to capture global image context. Vision Transformers (ViTs), inspired by Transformer models in natural language processing, use self-attention to capture long-range dependencies in images, offering potential for more accurate and reliable BC diagnosis.
Recent research explores hybrid CNN architectures and deep transfer learning to enhance classification accuracy on histopathological datasets like BreakHis. Evolutionary algorithms and data augmentation also contribute to optimization and performance improvements.
However, deep learning models are often criticized for their "black box" nature, which limits clinical trust and adoption. Explainable AI (XAI) methods, such as saliency maps, Grad-CAM, LIME, and SHAP, aim to make model decisions transparent by highlighting important image regions, thus increasing interpretability and clinician trust. Despite advances, XAI application specifically for mammography is underexplored, and there is a lack of standardized metrics to evaluate clinical relevance of explainability methods.
Integrating Vision Transformers with Explainable AI shows great promise. ViTs provide powerful feature extraction, while XAI reveals model reasoning, enabling clinicians to verify and understand diagnoses. Attention-based visualizations from ViTs, combined with other XAI techniques, can highlight diagnostic regions effectively, though challenges remain in computational demands and resolution of explanations.
Ultimately, this integration can lead to more transparent, trustworthy, and clinically useful AI systems for breast cancer detection, potentially improving patient outcomes.
the integration of Vision Transformers (ViTs) and Explainable Artificial Intelligence (XAI) into breast cancer histopathology analysis is an important step forward in the creation of intelligent, reliable, and clinically meaningful computer-aided diagnosis (CAD) systems. Vision Transformers have proven to be more effective than conventional convolutional neural networks (CNNs) at capturing global contextual features and long-range relationships. Their application in breast cancer classification has the potential to significantly enhance diagnostic accuracy across varying image magnifications and data complexities.At the same time, the growing emphasis on XAI reflects a critical shift toward interpretability and trustworthiness in deep learning models. In sensitive domains such as medical imaging, it is essential not only for models to perform well but further to give clear and logical justification for their forecasts. XAI methodsranging from saliency maps and Grad-CAM to more advanced tools like SHAP and LIMEare essential in helping to close the gap between clinical applicability and black-box algorithms by emphasizing pertinent information and offering numeric or visual explanations.Despite these advancements, several challenges remain. Current XAI research still lacks standardized, domain-specific evaluation metrics and often fails to address the unique requirements of breast imaging. Moreover, many existing studies focus primarily on model performance while giving limited attention to clinical integration and validation.Nevertheless, ongoing research continues to push the boundaries of what is possible, promising the future development of AI-driven systems that are not only accurate but also transparent, interpretable, and ultimately beneficial to patient care and clinical decision-making.
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Copyright © 2025 Laxmi Yadav, Girish Chandra, Divakar Yadav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET71641
Publish Date : 2025-05-26
ISSN : 2321-9653
Publisher Name : IJRASET
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