Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology

• URL: http://arxiv.org/abs/2206.05617v1
• Date: Sat, 11 Jun 2022 21:28:17 GMT
• Title: Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology
• Authors: Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti, Rushi Kulkarni, Steven Raman, Kirti Magudia, Corey W. Arnold, Peder E. Z. Larson
• Abstract summary: We introduce a flexible federated learning framework for cross-site training, validation, and evaluation of deep prostate cancer detection algorithms. Our results show increases in prostate cancer detection and classification accuracy using a specialized neural network model and diverse prostate biopsy data. We open-source our FLtools system, which can be easily adapted to other deep learning projects for medical imaging.
• Score: 3.8613414331251423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics. To enable this for prototype-stage algorithms, where the majority of existing research remains, in this paper we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of deep prostate cancer detection algorithms. Our approach utilizes an abstracted representation of the model architecture and data, which allows unpolished prototype deep learning models to be trained without modification using the NVFlare federated learning framework. Our results show increases in prostate cancer detection and classification accuracy using a specialized neural network model and diverse prostate biopsy data collected at two University of California research hospitals, demonstrating the efficacy of our approach in adapting to different datasets and improving MR-biomarker discovery. We open-source our FLtools system, which can be easily adapted to other deep learning projects for medical imaging.

Related papers

• TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis [3.262230127283452]
  Topological data analysis offers a unique approach by extracting essential information through the evaluation of topological patterns across different color channels. We show that the inclusion of topological features significantly improves the differentiation of tumor types in ovarian and breast cancers.
  arXiv  Detail & Related papers  (2024-10-13T12:24:13Z)
• Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification [0.0]
  Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. This work uses well-known DL models for the classification and segmentation of mpMRI images to detect PCa.
  arXiv  Detail & Related papers  (2023-10-09T03:00:15Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Unsupervised Pathology Detection: A Deep Dive Into the State of the Art [6.667150890634173]
  We evaluate a selection of cutting-edge Unsupervised Anomaly Detection (UAD) methods on multiple medical datasets. Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance. We show that such methods are capable of benefiting from recently developed self-supervised pre-training algorithms.
  arXiv  Detail & Related papers  (2023-03-01T16:03:25Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• 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)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI [0.0]
  We propose a machine learning-based method for improved voxel-wise PCa classification by taking into account the unique structures of the data. We describe detailed classification algorithm for the binary PCa status, as well as the ordinal clinical significance of PCa.
  arXiv  Detail & Related papers  (2020-07-02T00:14:55Z)
• A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images [21.627818410241552]
  A novel and efficient pancreatic tumor detection framework is proposed in this paper. The contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation Module. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge.
  arXiv  Detail & Related papers  (2020-02-11T15:48:22Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.