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Found 17 papers, 13 papers with code
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks
This paper presents a method for virtual contrast enhancement in breast MRI, offering a promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition.
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging.
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
This work addresses these challenges exploring and quantifying the utility of privacy-preserving deep learning techniques, concretely, (i) differentially private stochastic gradient descent (DP-SGD) and (ii) fully synthetic training data generated by our proposed malignancy-conditioned generative adversarial network.
Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks
We are able to outperform standard nnU-Net training, which is trained with constant patch size, in terms of Dice Score on 7 out of 10 MSD tasks while only spending roughly 50% of the original training runtime.
MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations
This dataset aims to accelerate the development and benchmarking of deep learning models and foster innovation in breast cancer diagnostics and treatment planning.
Mitigating annotation shift in cancer classification using single image generative models
However, the success of AI applications in this domain is restricted by the quantity and quality of available data, posing challenges due to limited and costly data annotation procedures that often lead to annotation shifts.
Debiasing Cardiac Imaging with Controlled Latent Diffusion Models
Notably, we conduct all our experiments using a single, consumer-level GPU to highlight the feasibility of our approach within resource-constrained environments.
Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making.
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Despite its benefits for tumour detection and treatment, the administration of contrast agents in dynamic contrast-enhanced MRI (DCE-MRI) is associated with a range of issues, including their invasiveness, bioaccumulation, and a risk of nephrogenic systemic fibrosis.
Revisiting Skin Tone Fairness in Dermatological Lesion Classification
Addressing fairness in lesion classification from dermatological images is crucial due to variations in how skin diseases manifest across skin tones.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
Towards Interpretable Summary Evaluation via Allocation of Contextual Embeddings to Reference Text Topics
Despite extensive recent advances in summary generation models, evaluation of auto-generated summaries still widely relies on single-score systems insufficient for transparent assessment and in-depth qualitative analysis.
medigan: a Python library of pretrained generative models for medical image synthesis
Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models in medical imaging.
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection.
Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification
Our experiments demonstrate that shared GANs notably increase the performance of both transformer and convolutional classification models and highlight this approach as a viable alternative to inter-centre data sharing.
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning.
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges.
