Taming the Tail: Leveraging Asymmetric Loss and Padé Approximation to Overcome Long-Tailed Class Imbalance


Pankhi Kashyap (Google), Pavni Tandon (Indian Institute of Technology, Bombay), Sunny Gupta (Indian Institute of Technology, Bombay), Abhishek Tiwari (Indian Institute of Technology, Bombay, Dhirubhai Ambani Institute Of Information and Communication Technology), Ritwik Kulkarni (Oraicle Biosciences LTD), Kshitij Sharad Jadhav (Indian Institute of Technology, Bombay)
The 35th British Machine Vision Conference
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Abstract

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 Padé 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.

Citation

@inproceedings{Kashyap_2024_BMVC,
author    = {Pankhi Kashyap and Pavni Tandon and Sunny Gupta and Abhishek Tiwari and Ritwik Kulkarni and Kshitij Sharad Jadhav},
title     = {Taming the Tail: Leveraging Asymmetric Loss and Padé Approximation to Overcome Long-Tailed Class Imbalance},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0416.pdf}
}


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