# Unmasking unlearnable models: a classification challenge for biomedical images without visible cues

arXiv: https://arxiv.org/abs/2407.19773 · HTML: https://mlsanity.com/paper/2407.19773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 29 Jul 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification](https://mlsanity.com/paper/2310.03485/) (2023)
- [Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://mlsanity.com/paper/1707.05425/) (2017)
- [Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI](https://mlsanity.com/paper/2302.00517/) (2023)
- [Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery](https://mlsanity.com/paper/2008.01218/) (2020)
- [Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features](https://mlsanity.com/paper/2109.12339/) (2021)
- [Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?](https://mlsanity.com/paper/2201.06086/) (2022)
- [MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network](https://mlsanity.com/paper/2210.03779/) (2022)
- [MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models](https://mlsanity.com/paper/2304.00774/) (2023)
- [Brain Tumor Radiogenomic Classification](https://mlsanity.com/paper/2401.09471/) (2024)
- [Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning](https://mlsanity.com/paper/2201.04416/) (2022)

## Abstract (arXiv)

Predicting traits from images lacking visual cues is challenging, as algorithms are designed to capture visually correlated ground truth. This problem is critical in biomedical sciences, and their solution can improve the efficacy of non-invasive methods. For example, a recent challenge of predicting MGMT methylation status from MRI images is critical for treatment decisions of glioma patients. Using less robust models poses a significant risk in these critical scenarios and underscores the urgency of addressing this issue. Despite numerous efforts, contemporary models exhibit suboptimal performance, and underlying reasons for this limitation remain elusive. In this study, we demystify the complexity of MGMT status prediction through a comprehensive exploration by performing benchmarks of existing models adjoining transfer learning. Their architectures were further dissected by observing gradient flow across layers. Additionally, a feature selection strategy was applied to improve model interpretability. Our finding highlighted that current models are unlearnable and may require new architectures to explore applications in the real world. We believe our study will draw immediate attention and catalyse advancements in predictive modelling with non-visible cues.