Aug 2026· European Archives of Oto-Rhino-Laryngology· Vol 283, pp. 6583 - 6592· 0 citations· 38 references
Medicine
TL;DR
The proposed IMIL-Net provides a high-accuracy, interpretable, and clinically-aligned diagnostic solution by analyzing complete patient examinations and providing a statistically validated decision-making process, and represents a trustworthy tool for integration into the clinical otolaryngology workflow.
A framework that showcases both deep and machine learning models will offer insight into the effectiveness of incorporating artificial intelligence in the screen accuracy of lung cancer classification, and an integrated risk-scoring framework that combines the imaging and clinical pipelines into a single interpretable...
Patthanan Uthaititpitak· Journal of Applied Research...· 0 citations
MagViT, an interpretable multi-magnification transformer framework with scale-gated fusion and patient-level model selection, is presented, an interpretable multi-magnification transformer framework with scale-gated fusion and patient-level model selection relative to prior ViT-centered BreakHis work.
Nabil Ashab, Soumitra Kundu, Saif Mahmud Parvez et al.· 0 citations
Due to its late identification and challenging diagnosis, lung cancer continues to be one of the top causes of death for cancer patients globally, positioning it as one of the most critical concerns. Timely identification of cancerous nodules is essential for enhancing the patient’s survival likelihood CT image analysi...
S. Jegadeesan, S. Matheswaran, R. Palanivelrajan· International Conference on...· 0 citations
The integration of these advanced techniques significantly enhances diagnostic reliability, addressing the challenges in histopathological image analysis, and proposes a novel explainable multi-model DL framework for breast cancer classification leveraging histopathological images.
Muhammad Nabeel Mehmood, Muhammad Hassaan Ashraf· Informatica· 0 citations
An optimized CCT (oCCT) is developed that addresses Computational and Architectural Complexity, as well as Local and Global Feature Learning and integrated explainable artificial intelligence to make oCCT easier for clinicians to trust and deploy, with clear explanations of its predictions.
An attention-enhanced deep learning framework for clinically accurate pneumonia identification from chest imaging radiology that combines a self-attention mechanism with a pretrained VGG16 backbone is proposed and tested against many cutting-edge convolutional neural network architectures.
Mohini Gahlot, Pinaki Ghosh· International journal of com...· 0 citations
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