# Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI

arXiv: https://arxiv.org/abs/2309.10725 · HTML: https://mlsanity.com/paper/2309.10725/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Interventional Few-Shot Learning](https://mlsanity.com/paper/2009.13000/) (2020)
- [Discovering Causal Signals in Images](https://mlsanity.com/paper/1605.08179/) (2016)
- [Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification](https://mlsanity.com/paper/2204.04567/) (2022)
- [Causal Deep Learning](https://mlsanity.com/paper/2303.02186/) (2023)
- [Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition](https://mlsanity.com/paper/2006.06889/) (2020)
- [Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution](https://mlsanity.com/paper/2102.11503/) (2021)

## Built on by (most cited first, 6 of 6)

- [CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning](https://mlsanity.com/paper/2408.04949/) (2024)
- [Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification](https://mlsanity.com/paper/2403.17530/) (2024)
- [Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations](https://mlsanity.com/paper/2402.04442/) (2024)
- [Enhanced Cascade Prostate Cancer Classifier in mp-MRI Utilizing Recall Feedback Adaptive Loss and Prior Knowledge-Based Feature Extraction](https://mlsanity.com/paper/2408.09746/) (2024)
- [Connectivity-Inspired Network for Context-Aware Recognition](https://mlsanity.com/paper/2409.04360/) (2024)
- [Causal Transfer in Medical Image Analysis](https://mlsanity.com/paper/2603.24388/) (2026)

## Abstract (arXiv)

In this paper, we present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module that extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme, where we propose a new meta-learning procedure entailing meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network's decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.