# Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images

arXiv: https://arxiv.org/abs/2603.12445 · HTML: https://mlsanity.com/paper/2603.12445/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [BCN20000: Dermoscopic Lesions in the Wild](https://mlsanity.com/paper/1908.02288/) (2019)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [Medical Image Analysis using Convolutional Neural Networks: A Review](https://mlsanity.com/paper/1709.02250/) (2017)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [Rethinking model prototyping through the MedMNIST+ dataset collection](https://mlsanity.com/paper/2404.15786/) (2024)
- [Identifying Bias in Deep Neural Networks Using Image Transforms](https://mlsanity.com/paper/2412.13079/) (2024)
- [Systematic biases when using deep neural networks for annotating large catalogs of astronomical images](https://mlsanity.com/paper/2201.03131/) (2022)
- [What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2201.08550/) (2022)

## Built on by (most cited first, 1 of 1)

- [Evaluating Contextual Bias in CNN Image Classification: Evidence from Agricultural Benchmark Datasets](https://mlsanity.com/paper/2609.14654/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature of CNNs makes it difficult to fully understand the way they operate, limiting their assessment to empirical evaluation. Here we study the soundness of the standard practices by which CNNs are evaluated for the purpose of cancer pathology. Thirteen highly used cancer benchmark datasets were analyzed, using four common CNN architectures and different types of cancer, such as melanoma, carcinoma, colorectal cancer, and lung cancer. We compared the accuracy of each model with that of datasets made of cropped segments from the background of the original images that do not contain clinically relevant content. Because the rendered datasets contain no clinical information, the null hypothesis is that the CNNs should provide mere chance-based accuracy when classifying these datasets. The results show that the CNN models provided high accuracy when using the cropped segments, sometimes as high as 93%, even though they lacked biomedical information. These results show that some CNN architectures are more sensitive to bias than others. The analysis shows that the common practices of machine learning evaluation might lead to unreliable results when applied to cancer pathology. These biases are very difficult to identify, and might mislead researchers as they use available benchmark datasets to test the efficacy of CNN methods.