Traditional image classification models heavily rely on accurately labeled data for training, but in real-world scenarios, acquiring large quantities of labeled images can be costly and time-consuming.

Aditionally, available datasets exhibits a notable class imbalance, with benign cases significantly outnumbering malignant ones. This imbalance is consistent with the epidemiological reality that the majority of individuals undergoing screening are found to be cancer-free, as malignancies occur in only a small subset of the tested population.

In this challenge, we provide you with a dataset that poses both obstacles: a significant portion of the training data remains unlabeled and the labeled data is heavily imbalanced.

Your task is to develop innovative deep learning algorithms and techniques to overcome these challenges and build a robust image classification model.

To succeed in this competition, participants are encouraged to explore semi-supervised/unsupervised learning methods that leverage the unlabeled data to improve the model's performance. Developing strategies to mitigate the impact of the class imbalance and enhance the model's ability to generalize effectively will be crucial. We encourage creative ideas.