OxML Challenge 2023: Carcinoma classification using data augmentation

TL;DR

This paper presents a novel data augmentation and ensembling approach for carcinoma classification in the context of the OxML 2023 challenge, effectively addressing data scarcity and imbalance issues.

Contribution

It introduces a combined padding augmentation and ensembling technique that improves classification performance on limited, imbalanced datasets.

Findings

Achieved top-three placement in the challenge

Demonstrated effectiveness of padding augmentation in medical image classification

Ensembling of neural networks enhances model robustness

Abstract

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.