SIENNA: Generalizable Lightweight Machine Learning Platform for Brain Tumor Diagnostics
Sunil, S.; Rajeev, R. S.; Chatterjee, A.; Pilitsis, J. G.; Mukherjee, A.; Paluh, J. L.
Show abstract
The transformative integration of Machine Learning (ML) for Artificial General Intelligence (AGI)-enhanced clinical imaging diagnostics, is itself in development. In brain tumor pathologies, magnetic resonance imaging (MRI) is a critical step that impacts the decision for invasive surgery, yet expert MRI tumor typing is inconsistent and misdiagnosis can reach levels as high as 85%. Current state-of-the-art (SOTA) ML brain tumor models struggle with data overfitting and susceptibility to shortcut learning, further exacerbated in large-sized models with many tunable parameters. In a comparison with multiple SOTA models, our deep ML brain tumor diagnostics model, SIENNA, surpassed limitations in four key areas of prioritized minimal data preprocessing, an optimized architecture that reduces shortcut learning and overfitting, integrated inductive cross-validation method for generalizability, and smaller neural architecture. SIENNA is applicable across MRI machines and 1.5 and 3.0 Tesla, and achieves high average accuracies on clinical DICOM MRI data across three-way classification: 92% (non-tumor), 91% (GBM), and 93% (MET) with retained high F1 and AUROC values for limited false positives/negatives. SIENNA is a lightweight clinical-ready AGI framework compatible with future multimodal expanded data integration.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids 96%
- A multimodal computational pipeline for 3D histology of the human brain 96%
- Deep neural networks allow expert-level brain meningioma detection, segmentation and improvement of current clinical practice 95%
Similar papers in this journal
- Prospective Motion Correction and Automatic Segmentation of Penetrating Arteries in Phase Contrast MRI at 7 T 95%
- Image- vs. histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images 95%
- Vendor-neutral sequences (VENUS) and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI 95%
Similar papers in this journal
- Anatomy-guided, modality-agnostic segmentation of neuroimaging abnormalities 97%
- OpenMAP-T1: A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain 96%
- WMH-DualTasker: A weakly-supervised deep learning model for automated white matter hyperintensities segmentation and visual rating prediction 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.