arXiv subjects

Emerson P. Grabke

Publications and source records attributed to Emerson P. Grabke.

2 recordsLinked to original sources

arXiv2026

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

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

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arXiv2026

Leveraging Clinical Text and Class Conditioning for 3D Prostate MRI Generation

Objective: Latent diffusion models (LDM) could alleviate data scarcity challenges affecting machine learning development for medical imaging. However, medical LDM strategies typically rely on short-prompt text encoders, nonmedical LDMs, or large data volumes. These strategies can limit performance and scientific accessibility. We propose a novel LDM conditioning approach to address these limitations. Methods: We propose Class-Conditioned Efficient Large Language model Adapter (CCELLA), a novel dual-head conditioning approach that simultaneously conditions the LDM U-Net with free-text clinical reports and radiology classification. We also propose a data-efficient LDM pipeline centered around CCELLA and a proposed joint loss function. We first evaluate our method on 3D prostate MRI against state-of-the-art. We then augment a downstream classifier model training dataset with synthetic images from our method. Results: Our method achieves a 3D FID score of 0.025 on a size-limited 3D prostate MRI dataset, significantly outperforming a recent foundation model with FID 0.070. When training a classifier for prostate cancer prediction, adding synthetic images generated by our method during training improves classifier accuracy from 69% to 74% and outperforms classifiers trained on images generated by prior state-of-the-art. Classifier training solely on our method's synthetic images achieved comparable performance to real image training. Conclusion: We show that our method improved both synthetic image quality and downstream classifier performance using limited data and minimal human annotation. Significance: The proposed CCELLA-centric pipeline enables radiology report and class-conditioned LDM training for high-quality medical image synthesis given limited data volume and human data annotation, improving LDM performance and scientific accessibility.

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