Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929529366249472 |
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| author | Kashyap, Pankhi Tandon, Pavni Gupta, Sunny Tiwari, Abhishek Kulkarni, Ritwik Jadhav, Kshitij Sharad |
| author_facet | Kashyap, Pankhi Tandon, Pavni Gupta, Sunny Tiwari, Abhishek Kulkarni, Ritwik Jadhav, Kshitij Sharad |
| contents | Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss functions such as cross-entropy and binary cross-entropy are often inadequate due to their inability to address the imbalances between the classes with high representation and the classes with low representation found in medical image datasets. We introduce a novel polynomial loss function based on Pade approximation, designed specifically to overcome the challenges associated with long-tailed classification. This approach incorporates asymmetric sampling techniques to better classify under-represented classes. We conducted extensive evaluations on three publicly available medical datasets and a proprietary medical dataset. Our implementation of the proposed loss function is open-sourced in the public repository:https://github.com/ipankhi/ALPA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance Kashyap, Pankhi Tandon, Pavni Gupta, Sunny Tiwari, Abhishek Kulkarni, Ritwik Jadhav, Kshitij Sharad Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.10; I.4.0; I.4.1; I.4.2; I.4.6; I.4.7; I.4.8; I.4.9; I.4.10; I.2.10; I.5.1; I.5.2; I.5.4; J.2; I.2.6; I.2.11; I.2.10 Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss functions such as cross-entropy and binary cross-entropy are often inadequate due to their inability to address the imbalances between the classes with high representation and the classes with low representation found in medical image datasets. We introduce a novel polynomial loss function based on Pade approximation, designed specifically to overcome the challenges associated with long-tailed classification. This approach incorporates asymmetric sampling techniques to better classify under-represented classes. We conducted extensive evaluations on three publicly available medical datasets and a proprietary medical dataset. Our implementation of the proposed loss function is open-sourced in the public repository:https://github.com/ipankhi/ALPA. |
| title | Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning I.2.10; I.4.0; I.4.1; I.4.2; I.4.6; I.4.7; I.4.8; I.4.9; I.4.10; I.2.10; I.5.1; I.5.2; I.5.4; J.2; I.2.6; I.2.11; I.2.10 |
| url | https://arxiv.org/abs/2410.04084 |