OxML Challenge 2023: Carcinoma classification using data augmentation
This work addresses the problem of limited and imbalanced medical data for carcinoma classification, which is incremental as it builds on existing techniques like ensembling and augmentation.
The paper tackled carcinoma classification on a small, imbalanced dataset from the OxML 2023 challenge by proposing a method combining padding augmentation and ensembling of neural networks, achieving a top-three placement and winning the challenge.
Carcinoma is the prevailing type of cancer and can manifest in various body parts. It is widespread and can potentially develop in numerous locations within the body. In the medical domain, data for carcinoma cancer is often limited or unavailable due to privacy concerns. Moreover, when available, it is highly imbalanced, with a scarcity of positive class samples and an abundance of negative ones. The OXML 2023 challenge provides a small and imbalanced dataset, presenting significant challenges for carcinoma classification. To tackle these issues, participants in the challenge have employed various approaches, relying on pre-trained models, preprocessing techniques, and few-shot learning. Our work proposes a novel technique that combines padding augmentation and ensembling to address the carcinoma classification challenge. In our proposed method, we utilize ensembles of five neural networks and implement padding as a data augmentation technique, taking into account varying image sizes to enhance the classifier's performance. Using our approach, we made place into top three and declared as winner.