mlsanityData to 7 Oct 2026

Towards Trust of Explainable AI in Thyroid Nodule Diagnosis

arXiv 2303.04731 ↗First posted 8 Mar 2023cs.CV cs.AIcited by 10

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Abstract (arXiv)

The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application. We propose new statistic-based XAI methods, namely Kernel Density Estimation and Density map, to explain the case of no nodule detected. XAI methods' performances are considered under a qualitative and quantitative comparison as feedback to improve the data quality and the model performance. Finally, we survey to assess doctors' and patients' trust in XAI explanations of the model's decisions on thyroid nodule images.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

19 of 35 references.

References · 19

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
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  6. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
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  10. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  11. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  12. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
7 more
  1. 2020Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNscited by 151
  2. 2020Restricting the Flow: Information Bottlenecks for Attributioncited by 110
  3. 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 74
  4. 2020Density Map Guided Object Detection in Aerial Imagescited by 56
  5. 2020Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregationcited by 13
  6. 2021Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networkscited by 11
  7. 2020Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Datasetcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 2; 2026: 1.

10 papers.

  1. 2023Towards Better Explanations for Object Detectioncited by 14
  2. 2024LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Taskscited by 12
  3. 2024XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approachcited by 7
  4. 2024Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainercited by 6
  5. 2025Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitorscited by 4
  6. 2025ODExAI: A Comprehensive Object Detection Explainable AI Evaluationcited by 3
  7. 2024Enhancing the Fairness and Performance of Edge Cameras with Explainable AIcited by 1
  8. 2024XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspectioncited by 1
  9. 2024FovEx: Human-Inspired Explanations for Vision Transformers and Convolutional Neural Networkscited by 1
  10. 2026Adaptive Arena-based Contestable Argumentative Network-of-Experts for Open-Ended Care Plan Coordinationcited by 0