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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01202 |
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| _version_ | 1866917973814411264 |
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| author | Dhaubhadel, Sayera Mohd-Yusof, Jamaludin McMahon, Benjamin H. Estrada, Trilce Ganguly, Kumkum Spannaus, Adam Gounley, John P. Wu, Xiao-Cheng Durbin, Eric B. Hanson, Heidi A. Bhattacharya, Tanmoy |
| author_facet | Dhaubhadel, Sayera Mohd-Yusof, Jamaludin McMahon, Benjamin H. Estrada, Trilce Ganguly, Kumkum Spannaus, Adam Gounley, John P. Wu, Xiao-Cheng Durbin, Eric B. Hanson, Heidi A. Bhattacharya, Tanmoy |
| contents | We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based deep abstaining classifier (DAC), for automated annotation of cancer pathology reports from NCI-SEER registries. Our classifier was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and/or confusing classes to achieve a target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations, enabled global identification of sources of errors as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies such as exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes to iteratively improve our DAC in this complex real-world implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Global explainability of a deep abstaining classifier Dhaubhadel, Sayera Mohd-Yusof, Jamaludin McMahon, Benjamin H. Estrada, Trilce Ganguly, Kumkum Spannaus, Adam Gounley, John P. Wu, Xiao-Cheng Durbin, Eric B. Hanson, Heidi A. Bhattacharya, Tanmoy Machine Learning We present a global explainability method to characterize sources of errors in the histology prediction task of our real-world multitask convolutional neural network (MTCNN)-based deep abstaining classifier (DAC), for automated annotation of cancer pathology reports from NCI-SEER registries. Our classifier was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and/or confusing classes to achieve a target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations, enabled global identification of sources of errors as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies such as exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes to iteratively improve our DAC in this complex real-world implementation. |
| title | Global explainability of a deep abstaining classifier |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.01202 |