Analyzing explainability of YOLO-based breast cancer detection using heat map visualizations

The integration of YOLO11 with HiResCAM offers a robust solution that combines high detection accuracy with improved model interpretability. This approach not only enhances user trustworthiness by revealing decision-making patterns and limitations but also provide insights into the weaknesses of the model, enabling developers to refine and improve AI performance further.

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Publication Details

Journal
Quantitative Imaging in Medicine and Surgery
Published
2025-07-01
DOI
https://doi.org/10.21037/qims-2024-2911
Citations
3
Primary Topic
Infrared Thermography in Medicine
Type
article
Field-Weighted Citation Impact
4.08
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article

Analyzing explainability of YOLO-based breast cancer detection using heat map visualizations

May Phu Paing, Awika Ariyametkul
3 citations
Quantitative Imaging in Medicine and Surgery
Infrared Thermography in Medicine
4.08
article

Analyzing explainability of YOLO-based breast cancer detection using heat map visualizations

May Phu Paing, Awika Ariyametkul
article en
3 citations

Abstract

The integration of YOLO11 with HiResCAM offers a robust solution that combines high detection accuracy with improved model interpretability. This approach not only enhances user trustworthiness by revealing decision-making patterns and limitations but also provide insights into the weaknesses of the model, enabling developers to refine and improve AI performance further.

Quantitative Imaging in Medicine and SurgeryVol. 15(7)
King Mongkut's Institute of Technology Ladkrabang (TH)
Openalex Percentile: Top 6%
Infrared Thermography in Medicine
4.08
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