Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

• URL: http://arxiv.org/abs/2203.16622v2
• Date: Fri, 1 Apr 2022 02:19:40 GMT
• Title: Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
• Authors: Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc, Rajarsi Gupta, Erich Bremer, Shahira Abousamra, Siddhesh P. Thakur, Joel H. Saltz, Spyridon Bakas
• Abstract summary: We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. Deep learning classification model was trained using 50*50 square micron patches extracted from whole slide images.
• Score: 5.881088147423591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distribution of tumor infiltrating lymphocytes within whole slide images (WSIs). A deep learning classification model was trained using 50*50 square micron patches extracted from the WSIs. We simulated a FL environment in which a dataset, generated from WSIs of cancer from numerous anatomical sites available by The Cancer Genome Atlas repository, is partitioned in 8 different nodes. Our results show that the model trained with the federated training approach achieves similar performance, both quantitatively and qualitatively, to that of a model trained with all the training data pooled at a centralized location. Our study shows that FL has tremendous potential for enabling development of more robust and accurate models for histopathology image analysis without having to collect large and diverse training data at a single location.

Related papers

• S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences [91.48841778012782]
  We propose an unsupervised method to simultaneously learn local and global shape structures across population anatomies. Our pipeline significantly improves unsupervised correspondence estimation for SSMs compared to baseline methods. Our method is robust enough to learn from noisy neural network predictions, potentially enabling scaling SSMs to larger patient populations.
  arXiv  Detail & Related papers  (2023-04-15T09:39:52Z)
• 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)
• CXR-FL: Deep Learning-based Chest X-ray Image Analysis Using Federated Learning [0.0]
  We present an evaluation of deep learning-based models for chest X-ray image analysis using the federated learning method. We show that classification models perform worse if trained on a region of interest reduced to segmentation of the lung compared to the full image.
  arXiv  Detail & Related papers  (2022-04-11T15:47:54Z)
• PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning [20.940530194934972]
  Pyramidal Deep-Broad Learning (PDBL) is a lightweight plug-and-play module for any well-trained classification backbone. PDBL can steadily improve the tissue-level classification performance for any CNN backbones.
  arXiv  Detail & Related papers  (2021-11-04T09:35:12Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Federated Learning for Breast Density Classification: A Real-World Implementation [19.03378677235258]
  Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS) We show that despite substantial differences among the datasets from all sites, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone.
  arXiv  Detail & Related papers  (2020-09-03T18:34:59Z)
• Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results [13.615292855384729]
  To train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. Due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data.
  arXiv  Detail & Related papers  (2020-01-16T04:49:33Z)

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.