# Soft-CAM: Making black box models self-explainable for medical image analysis Source: [Soft-CAM: Making black box models self-explainable for medical image analysis]() Collection index: [llms.txt]() Data updated: 2026-02-24T05:00:04.779Z Authors: Kerol Djoumessi, Philipp Berens Published: 2025-05-23T11:15:21.000Z ## 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 results demonstrate that CNNs can be inherently interpretable without compromising performance, advancing the development of self-explainable deep learning for high-stakes decision-making. The code is available at https://github.com/kdjoumessi/SoftCAM ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Top-k localization precision and sensitivity on OCT](): Top-k localization precision and sensitivity; OCT; Top-k Precision: 86. - [Top-k localization precision and sensitivity on RSNA](): Top-k localization precision and sensitivity; RSNA; Top-k Precision: 73. - [Top-k Localization Precision and Sensitivity on Fundus](): Top-k localization precision and sensitivity; Fundus; Top-k Precision: 65. - [Model Explainability Faithfulness on Fundus](): Model Explainability Faithfulness; Fundus; AUDC: 0.905. - [Model Explainability Faithfulness on OCT](): Model Explainability Faithfulness; OCT; AUDC: 82.6. ## Original sources and implementation [Original paper]() [PDF]()