# Mitigating 3D Prostate Biparametric MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

arXiv: https://arxiv.org/abs/2507.06384 · HTML: https://mlsanity.com/paper/2507.06384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 21 of 21)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis](https://mlsanity.com/paper/1804.06353/) (2018)
- [MAISI: Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2409.11169/) (2024)
- [CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis](https://mlsanity.com/paper/2303.14081/) (2023)
- [Cross-conditioned Diffusion Model for Medical Image to Image Translation](https://mlsanity.com/paper/2409.08500/) (2024)
- [Medical diffusion on a budget: Textual Inversion for medical image generation](https://mlsanity.com/paper/2303.13430/) (2023)
- [Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI](https://mlsanity.com/paper/2112.05151/) (2021)
- [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://mlsanity.com/paper/2311.11383/) (2023)
- [Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion](https://mlsanity.com/paper/2303.02094/) (2023)
- [Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis](https://mlsanity.com/paper/2409.13532/) (2024)
- [A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI](https://mlsanity.com/paper/1909.09541/) (2019)
- [Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generation](https://mlsanity.com/paper/2506.10230/) (2025)

## Abstract (arXiv)

Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpretation. The recent CCELLA LDM improved prostate cancer detection performance using synthetic MRI for classifier training but was limited to the axial T2-weighted (AxT2) sequence, did not investigate inter-institutional domain shift, and prioritized PI-RADS over histopathology outcomes. Methods: We propose CCELLA++, a novel LDM pipeline for simultaneous 3D biparametric prostate MRI (bpMRI) generation, including the AxT2, high b-value diffusion series (HighB) and apparent diffusion coefficient map (ADC), to overcome these limitations. We investigated source-free domain adaptation with classifiers pretrained on single institution real or LDM-generated synthetic data prior to fine-tuning on fractions of an out-of-distribution, external dataset. Results: CCELLA++ achieved comparable AxT2 Kernel Inception Distance to CCELLA (0.0128, 0.0131 respectively). CCELLA++ synthetic bpMRI pretraining outperformed real bpMRI in AP and AUC up to 12.5% (n<=166) external dataset volume (p<0.01 all), no pretraining in AUC up to 25% external volume (n=332, p<0.05 all), and CCELLA AxT2-only pretraining in both data-scarce (n=83, p<0.001 AP and AUC) and full data (n=1329, p<0.05 AP and AUC) scenarios. Conclusion: CCELLA++ synthetic bpMRI can improve downstream classifier generalization and performance beyond real bpMRI or CCELLA-generated AxT2-only images. Future work should quantify medical image quality, balance bpMRI LDM training, and condition the LDM with additional information. Significance: CCELLA++ can generate synthetic bpMRI that outperforms real data for domain adaptation with data-scarce external institutions, advancing machine learning development for medical imaging. Our code is available at https://github.com/grabkeem/CCELLA-plus-plus