# GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI

arXiv: https://arxiv.org/abs/2008.04396 · HTML: https://mlsanity.com/paper/2008.04396/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation](https://mlsanity.com/paper/2103.12857/) (2021)
- [Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects](https://mlsanity.com/paper/2311.02332/) (2023)
- [PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models](https://mlsanity.com/paper/2405.16942/) (2024)
- [Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification](https://mlsanity.com/paper/2204.02399/) (2022)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis](https://mlsanity.com/paper/2508.01292/) (2025)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)
- [Diffusion Bridge Networks Simulate Clinical-grade PET from MRI for Dementia Diagnostics](https://mlsanity.com/paper/2510.15556/) (2025)
- [Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease](https://mlsanity.com/paper/2511.21114/) (2025)
- [Translating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness](https://mlsanity.com/paper/2603.18896/) (2026)
- [Bilevel Hypergraph Networks for Multi-Modal Alzheimer's Diagnosis](https://mlsanity.com/paper/2403.12719/) (2024)
- [Long-Term Alzheimers Disease Prediction: A Novel Image Generation Method Using Temporal Parameter Estimation with Normal Inverse Gamma Distribution on Uneven Time Series](https://mlsanity.com/paper/2511.21057/) (2025)
- [Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge](https://mlsanity.com/paper/2609.20147/) (2026)

## Abstract (arXiv)

Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution. Unfortunately, the diagnosis of AD using MRI is difficult due to the very subtle physiological differences between healthy and AD subjects visible on MRI. As a result, many attempts have been made to synthesize PET images from MR images using generative adversarial networks (GANs) in the interest of enabling the diagnosis of AD from MR. Existing work on PET synthesis from MRI has largely focused on Conditional GANs, where MR images are used to generate PET images and subsequently used for AD diagnosis. There is no end-to-end training goal. This paper proposes an alternative approach to the aforementioned, where AD diagnosis is incorporated in the GAN training objective to achieve the best AD classification performance. Different GAN lossesare fine-tuned based on the discriminator performance, and the overall training is stabilized. The proposed network architecture and training regime show state-of-the-art performance for three- and four- class AD classification tasks.