# OxML Challenge 2023: Carcinoma classification using data augmentation

arXiv: https://arxiv.org/abs/2409.10544 · HTML: https://mlsanity.com/paper/2409.10544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Natural Language Processing Advancements By Deep Learning: A Survey](https://mlsanity.com/paper/2003.01200/) (2020)
- [Medical Image Registration Using Deep Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2002.03401/) (2020)
- [Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets](https://mlsanity.com/paper/2210.00824/) (2022)
- [AudRandAug: Random Image Augmentations for Audio Classification](https://mlsanity.com/paper/2309.04762/) (2023)
- [KeepOriginalAugment: Single Image-based Better Information-Preserving Data Augmentation Approach](https://mlsanity.com/paper/2405.06354/) (2024)
- [An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans](https://mlsanity.com/paper/2305.10115/) (2023)

## Built on by (most cited first, 1 of 1)

- [FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation](https://mlsanity.com/paper/2410.14070/) (2024)

## Abstract (arXiv)

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.