Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning
Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study exa...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6883 |
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| author | Suphakon Jarujunawong Paramate Horkaew |
| author_facet | Suphakon Jarujunawong Paramate Horkaew |
| author_sort | Suphakon Jarujunawong |
| collection | DOAJ |
| description | Artificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study examines the pivotal role of multi-band visual-enhanced features in invasive ductal carcinoma classification using whole slide imaging. Our results showed that orientation invariant filters achieved an accuracy of 0.8125, F1-score of 0.8134, and AUC of 0.8761, while preserving cellular arrangement and tissue morphology. By utilizing spatial relationships across varying extents, the proposed fusion strategy aligns with pathological interpretation principles. While integrating Gabor wavelet responses into ResNet-50 enhanced feature association, the comparative analysis emphasized the benefits of weighted morphological fusion, further strengthening diagnostic performance. These insights underscore the crucial role of informative filters in advancing DL schemes for breast cancer screening. Future research incorporating diverse, multi-center datasets could further validate the approach and broaden its diagnostic applications. |
| format | Article |
| id | doaj-art-d1dd4dc6c84040e791f237f34ff418a2 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d1dd4dc6c84040e791f237f34ff418a22025-08-20T02:24:39ZengMDPI AGApplied Sciences2076-34172025-06-011512688310.3390/app15126883Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep LearningSuphakon Jarujunawong0Paramate Horkaew1School of Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandArtificial intelligence is increasingly shaping the landscape of computer-aided diagnosis of breast cancer. Despite incrementally improved accuracy, pathologist supervision remains essential for verified interpretation. While prior research focused on devising deep model architecture, this study examines the pivotal role of multi-band visual-enhanced features in invasive ductal carcinoma classification using whole slide imaging. Our results showed that orientation invariant filters achieved an accuracy of 0.8125, F1-score of 0.8134, and AUC of 0.8761, while preserving cellular arrangement and tissue morphology. By utilizing spatial relationships across varying extents, the proposed fusion strategy aligns with pathological interpretation principles. While integrating Gabor wavelet responses into ResNet-50 enhanced feature association, the comparative analysis emphasized the benefits of weighted morphological fusion, further strengthening diagnostic performance. These insights underscore the crucial role of informative filters in advancing DL schemes for breast cancer screening. Future research incorporating diverse, multi-center datasets could further validate the approach and broaden its diagnostic applications.https://www.mdpi.com/2076-3417/15/12/6883computer-aided diagnosisdeep learningexplainable AIGabor waveletwhole slide imaging |
| spellingShingle | Suphakon Jarujunawong Paramate Horkaew Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning Applied Sciences computer-aided diagnosis deep learning explainable AI Gabor wavelet whole slide imaging |
| title | Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning |
| title_full | Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning |
| title_fullStr | Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning |
| title_full_unstemmed | Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning |
| title_short | Enhancing AI-Driven Diagnosis of Invasive Ductal Carcinoma with Morphologically Guided and Interpretable Deep Learning |
| title_sort | enhancing ai driven diagnosis of invasive ductal carcinoma with morphologically guided and interpretable deep learning |
| topic | computer-aided diagnosis deep learning explainable AI Gabor wavelet whole slide imaging |
| url | https://www.mdpi.com/2076-3417/15/12/6883 |
| work_keys_str_mv | AT suphakonjarujunawong enhancingaidrivendiagnosisofinvasiveductalcarcinomawithmorphologicallyguidedandinterpretabledeeplearning AT paramatehorkaew enhancingaidrivendiagnosisofinvasiveductalcarcinomawithmorphologicallyguidedandinterpretabledeeplearning |