Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification

• URL: http://arxiv.org/abs/2203.04961v1
• Date: Tue, 8 Mar 2022 19:37:08 GMT
• Title: Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification
• Authors: Zuzanna Szafranowska, Richard Osuala, Bennet Breier, Kaisar Kushibar, Karim Lekadir, Oliver Diaz
• Abstract summary: Deep-learning based computer-aided detection systems show promising potential in improving the curability and mortality rates of breast cancer. Many clinical centres are restricted in the amount and heterogeneity of available data to train such models. We propose sharing trained generative models between centres as substitute for real patient data.
• Score: 5.431631427493169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such models to (i) achieve promising performance and to (ii) generalise well across acquisition protocols and domains. As sharing data between centres is restricted due to patient privacy concerns, we propose a potential solution: sharing trained generative models between centres as substitute for real patient data. In this work, we use three well known mammography datasets to simulate three different centres, where one centre receives the trained generator of Generative Adversarial Networks (GANs) from the two remaining centres in order to augment the size and heterogeneity of its training dataset. We evaluate the utility of this approach on mammography patch classification on the test set of the GAN-receiving centre using two different classification models, (a) a convolutional neural network and (b) a transformer neural network. 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.

Related papers

• Abnormality-Driven Representation Learning for Radiology Imaging [0.8321462983924758]
  We introduce lesion-enhanced contrastive learning (LeCL), a novel approach to obtain visual representations driven by abnormalities in 2D axial slices across different locations of the CT scans. We evaluate our approach across three clinical tasks: tumor lesion location, lung disease detection, and patient staging, benchmarking against four state-of-the-art foundation models.
  arXiv  Detail & Related papers  (2024-11-25T13:53:26Z)
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study [49.5374512525016]
  Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. Some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. We propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
  arXiv  Detail & Related papers  (2024-01-18T16:59:27Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology [9.037868656840736]
  In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features. This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
  arXiv  Detail & Related papers  (2022-04-18T13:31:53Z)
• Deep learning based domain adaptation for mitochondria segmentation on EM volumes [5.682594415267948]
  We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain. We propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.
  arXiv  Detail & Related papers  (2022-02-22T09:49:25Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound [1.9814912982226993]
  We propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. The proposed approach is based on a GoogleNet architecture, fine-tuned for our data in two training steps. It achieves an AUC of 0.94, outperforming state-of-the-art models trained over a single modality.
  arXiv  Detail & Related papers  (2020-09-23T09:08:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.