ReviewPhysics in medicine and biology2021
Interpretation and visualization techniques for deep learning models in medical imaging.
Review in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2022Pooled it
- Signal-carrying speckle in optical coherence tomography: a methodological review on biomedical applications.Journal of biomedical optics · 2022Pooled it
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- Machine Learning-Guided Near-Infrared Laser Processing for Precise Geometry Control in Microneedles with Downstream microLED-Triggered Photothermal Functionality.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.Scientific reports · 2026Article
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- Deep learning-based histopathological classification and subclassification of benign and malignant salivary gland tumors.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.Journal of medical Internet research · 2026Article
- Automatic Recognition and Prognostic Prediction of Colorectal Liver Metastases Using a Multi-Scale Deep Learning Framework: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Advancements in Developing an Automated Breast Density Detection Technique for Breast Cancer Risk Prediction: Synthesizing a Signal-dependent Noise Stochastic Process.bioRxiv : the preprint server for biology · 2026Article
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- Radiomics-machine learning model for predicting invasiveness of subcentimeter subsolid lung adenocarcinoma: a validation study with external cohort and SHAP interpretability.Frontiers in oncology · 2026Article
- Brain age prediction and early neurodegeneration detection using contrastive learning on brain biomechanics: a retrospective, multicentre study.EBioMedicine · 2025Article
- Lightweight Deep Learning Models with Explainable AI for Early Alzheimer's Detection from Standard MRI Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Influence of preprocessing of stimulated Raman scattering images on the performance of deep neural networks for detecting cancer tissue.Quantitative imaging in medicine and surgery · 2025Article
- Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.Scientific reports · 2025Article
- Counterfactual Diffusion Models for Interpretable Morphology-based Explanations of Artificial Intelligence Models in Pathology.bioRxiv : the preprint server for biology · 2025Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Deep learning (DL) approaches to medical image analysis tasks have recently become popular; however, they suffer from a lack of human interpretability critical for both increasing understanding of the methods' operation and enabling clinical translation. This review summarizes currently available methods for performing image model interpretation and critically evaluates published uses of these methods for medical imaging applications. We divide model interpretation in two categories: (1) understanding model structure and function and (2) understanding model output. Understanding model structure and function summarizes ways to inspect the learned features of the model and how those features act on an image. We discuss techniques for reducing the dimensionality of high-dimensional data and cover autoencoders, both of which can also be leveraged for model interpretation. Understanding model output covers attribution-based methods, such as saliency maps and class activation maps, which produce heatmaps describing the importance of different parts of an image to the model prediction. We describe the mathematics behind these methods, give examples of their use in medical imaging, and compare them against one another. We summarize several published toolkits for model interpretation specific to medical imaging applications, cover limitations of current model interpretation methods, provide recommendations for DL practitioners looking to incorporate model interpretation into their task, and offer general discussion on the importance of model interpretation in medical imaging contexts.
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What OpenQuestion holds
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.