dorsal/arxiv
View SchemaKidney Cancer Detection Using 3D-Based Latent Diffusion Models
| Authors | Jen Dusseljee, Sarah de Boer, Alessa Hering |
|---|---|
| Categories | |
| ArXiv ID | 2601.05852vv1 |
| URL | https://arxiv.org/abs/2601.05852 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusion Implicit Models (DDIMs), and Vector-Quantized Generative Adversarial Networks (VQ-GANs). Unlike prior slice-wise approaches, our method operates directly on an image volume and leverages weak supervision with only case-level pseudo-labels. We benchmark our approach against state-of-the-art supervised segmentation and detection models. This study demonstrates the feasibility and promise of 3D latent diffusion for weakly supervised anomaly detection. While the current results do not yet match supervised baselines, they reveal key directions for improving reconstruction fidelity and lesion localization. Our findings provide an important step toward annotation-efficient, generative modeling of complex abdominal anatomy.
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"date_created": "2026-02-17T05:53:04.062000Z",
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"abstract": "In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusion Implicit Models (DDIMs), and Vector-Quantized Generative Adversarial Networks (VQ-GANs). Unlike prior slice-wise approaches, our method operates directly on an image volume and leverages weak supervision with only case-level pseudo-labels. We benchmark our approach against state-of-the-art supervised segmentation and detection models. This study demonstrates the feasibility and promise of 3D latent diffusion for weakly supervised anomaly detection. While the current results do not yet match supervised baselines, they reveal key directions for improving reconstruction fidelity and lesion localization. Our findings provide an important step toward annotation-efficient, generative modeling of complex abdominal anatomy.",
"arxiv_id": "2601.05852",
"authors": [
"Jen Dusseljee",
"Sarah de Boer",
"Alessa Hering"
],
"categories": [
"cs.CV"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Kidney Cancer Detection Using 3D-Based Latent Diffusion Models",
"url": "https://arxiv.org/abs/2601.05852",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "7223a163-6dd5-432e-b894-6eb4ab354247",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
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