# Exploring learning environments for label-efficient cancer diagnosis

arXiv: https://arxiv.org/abs/2408.07988 · HTML: https://mlsanity.com/paper/2408.07988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation](https://mlsanity.com/paper/1706.04737/) (2017)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [An Overview of Deep Semi-Supervised Learning](https://mlsanity.com/paper/2006.05278/) (2020)
- [Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.00962/) (2022)
- [Reducing Label Effort: Self-Supervised meets Active Learning](https://mlsanity.com/paper/2108.11458/) (2021)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection](https://mlsanity.com/paper/2205.00122/) (2022)
- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [Systematic comparison of semi-supervised and self-supervised learning for medical image classification](https://mlsanity.com/paper/2307.08919/) (2023)
- [Semi-supervised multi-task learning for lung cancer diagnosis](https://mlsanity.com/paper/1802.06181/) (2018)
- [Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases](https://mlsanity.com/paper/1906.09587/) (2019)
- [Using VGG16 Algorithms for classification of lung cancer in CT scans Image](https://mlsanity.com/paper/2305.18367/) (2023)
- [Comparative Analysis of Deep Learning Architectures for Breast Cancer Diagnosis Using the BreaKHis Dataset](https://mlsanity.com/paper/2309.01007/) (2023)
- [Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate](https://mlsanity.com/paper/2304.00049/) (2023)

## Abstract (arXiv)

Despite significant research efforts and advancements, cancer remains a leading cause of mortality. Early cancer prediction has become a crucial focus in cancer research to streamline patient care and improve treatment outcomes. Manual tumor detection by histopathologists can be time consuming, prompting the need for computerized methods to expedite treatment planning. Traditional approaches to tumor detection rely on supervised learning, necessitates a large amount of annotated data for model training. However, acquiring such extensive labeled data can be laborious and time-intensive. This research examines the three learning environments: supervised learning (SL), semi-supervised learning (Semi-SL), and self-supervised learning (Self-SL): to predict kidney, lung, and breast cancer. Three pre-trained deep learning models (Residual Network-50, Visual Geometry Group-16, and EfficientNetB0) are evaluated based on these learning settings using seven carefully curated training sets. To create the first training set (TS1), SL is applied to all annotated image samples. Five training sets (TS2-TS6) with different ratios of labeled and unlabeled cancer images are used to evaluateSemi-SL. Unlabeled cancer images from the final training set (TS7) are utilized for Self-SL assessment. Among different learning environments, outcomes from the Semi-SL setting show a strong degree of agreement with the outcomes achieved in the SL setting. The uniform pattern of observations from the pre-trained models across all three datasets validates the methodology and techniques of the research. Based on modest number of labeled samples and minimal computing cost, our study suggests that the Semi-SL option can be a highly viable replacement for the SL option under label annotation constraint scenarios.