ArticleBioengineering (Basel, Switzerland)2023
Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
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- Deep learning in enhanced CT imaging: predicting invasion depth of rectal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Deep learning-based automated classification of endometrial lesions in IVF patients using hysteroscopic images.Scientific reports · 2026Article
- Developing Synthetic Orthopantomogram Datasets Through Generative Models.International dental journal · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Swin Transformer-based intelligent fracture classification and radiographic assessment to support clinical decision-making.BMC medical informatics and decision making · 2026Article
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- Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data.Frontiers in immunology · 2026Article
- Ultrasound-based deep learning radiomics for the differential diagnosis of benign and malignant subpleural pulmonary lesions.Frontiers in oncology · 2026Article
- Application of deep learning-clinical baseline feature fusion model to predict postoperative mortality in elderly patients with hip fracture: a multicenter study.Frontiers in medicine · 2026Article
- Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification.MethodsX · 2025Article
- Attention mechanism-based multi-parametric MRI ensemble model for predicting tumor budding grade in rectal cancer patients.Abdominal radiology (New York) · 2025Article
- Preoperative prediction of malignant transformation in sinonasal inverted papilloma: a novel MRI-based deep learning approach.European radiology · 2025Article
- Beyond Binary: A Machine Learning Framework for Interpreting Organismal Behavior in Cancer Diagnostics.Biomedicines · 2025Review
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Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The opacity of deep learning makes its application challenging in the medical field. Therefore, there is a need to enable explainable artificial intelligence (XAI) in the medical field to ensure that models and their results can be explained in a manner that humans can understand. This study uses a high-accuracy computer vision algorithm model to transfer learning to medical text tasks and uses the explanatory visualization method known as gradient-weighted class activation mapping (Grad-CAM) to generate heat maps to ensure that the basis for decision-making can be provided intuitively or via the model. The system comprises four modules: pre-processing, word embedding, classifier, and visualization. We used Word2Vec and BERT to compare word embeddings and use ResNet and 1Dimension convolutional neural networks (CNN) to compare classifiers. Finally, the Bi-LSTM was used to perform text classification for direct comparison. With 25 epochs, the model that used pre-trained ResNet on the formalized text presented the best performance (recall of 90.9%, precision of 91.1%, and an F1 score of 90.2% weighted). This study uses ResNet to process medical texts through Grad-CAM-based explainable artificial intelligence and obtains a high-accuracy classification effect; at the same time, through Grad-CAM visualization, it intuitively shows the words to which the model pays attention when making predictions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.