Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability
<|reference_start|>Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability: It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating diverse data sources beyond conventional imaging. Using multi-modal techniques, integrating both image and non-image data, marks a transformative advancement in breast cancer diagnosis. The purpose of this review is to explore the burgeoning field of multimodal techniques, particularly the fusion of histopathology images with non-image data. Further, Explainable AI (XAI) will be used to elucidate the decision-making processes of complex algorithms, emphasizing the necessity of explainability in diagnostic processes. This review utilizes multi-modal data and emphasizes explainability to enhance diagnostic accuracy, clinician confidence, and patient engagement, ultimately fostering more personalized treatment strategies for breast cancer, while also identifying research gaps in multi-modality and explainability, guiding future studies, and contributing to the strategic direction of the field.<|reference_end|>
@article{abdullakutty2024advancing,
    title={Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into
  Multi-Modality and Explainability},
    author={Faseela Abdullakutty, Younes Akbari, Somaya Al-Maadeed, Ahmed
  Bouridane and Rifat Hamoudi},
    journal={arXiv preprint arXiv:2406.12897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12897},
    primaryClass={cs.LG cs.AI cs.CV}
}