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[2505.17748] Soft-CAM: Making black box models self-explainable for medical image analysis
Summary
The paper introduces Soft-CAM, a method that enhances the interpretability of convolutional neural networks (CNNs) in medical image analysis without sacrificing performance.
Why It Matters
In high-stakes fields like medicine, the ability to understand AI decision-making is crucial. Soft-CAM addresses the limitations of traditional post-hoc explanation methods, offering a more reliable way to interpret CNNs, thus fostering trust in AI applications in healthcare.
Key Takeaways
- Soft-CAM improves CNN interpretability by modifying architecture.
- It replaces global average pooling with a convolution-based class evidence layer.
- The method maintains classification performance while enhancing explanation quality.
- Evaluated on three medical datasets, it shows significant improvements over existing methods.
- Soft-CAM promotes self-explainable deep learning for critical applications.
Computer Science > Machine Learning arXiv:2505.17748 (cs) [Submitted on 23 May 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Soft-CAM: Making black box models self-explainable for medical image analysis Authors:Kerol Djoumessi, Philipp Berens View a PDF of the paper titled Soft-CAM: Making black box models self-explainable for medical image analysis, by Kerol Djoumessi and 1 other authors View PDF HTML (experimental) Abstract:Convolutional neural networks (CNNs) are widely used for high-stakes applications like medicine, often surpassing human performance. However, most explanation methods rely on post-hoc attribution, approximating the decision-making process of already trained black-box models. These methods are often sensitive, unreliable, and fail to reflect true model reasoning, limiting their trustworthiness in critical applications. In this work, we introduce SoftCAM, a straightforward yet effective approach that makes standard CNN architectures inherently interpretable. By removing the global average pooling layer and replacing the fully connected classification layer with a convolution-based class evidence layer, SoftCAM preserves spatial information and produces explicit class activation maps that form the basis of the model's predictions. Evaluated on three medical datasets, SoftCAM maintains classification performance while significantly improving both the qualitative and quantitative explanation compared to existing post-hoc methods. Our result...