The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions

• URL: http://arxiv.org/abs/2011.08096v1
• Date: Mon, 16 Nov 2020 16:57:05 GMT
• Title: The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
• Authors: Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Arun, Liangqiong Qu, Katharina Hoebel, Jay Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L Rubin and Jayashree Kalpathy-Cramer
• Abstract summary: We investigate trade-off between model refinement and retention of previously learned knowledge. We propose a simple yet effective approach, adapting Elastic weight consolidation (EWC) using the global batch normalization statistics of the original dataset.
• Score: 8.244654685687054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types. While simply training on the combined datasets is fraught with data privacy limitations, fine-tuning the model on subsequent institutions after training it on the original institution results in a decrease in performance on the original dataset, a phenomenon called catastrophic forgetting. In this paper, we investigate trade-off between model refinement and retention of previously learned knowledge and subsequently address catastrophic forgetting for the assessment of mammographic breast density. More specifically, we propose a simple yet effective approach, adapting Elastic weight consolidation (EWC) using the global batch normalization (BN) statistics of the original dataset. The results of this study provide guidance for the deployment of clinical deep learning models where continuous learning is needed for domain expansion.

Related papers

• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• 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)
• Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI [11.314017805825685]
  We propose a divergence-aware dual-flow module with balanced rigidity and plasticity branches to decouple old and new tasks. We evaluate our framework on a brain tumor segmentation task with continually changing target domains.
  arXiv  Detail & Related papers  (2023-05-30T20:39:03Z)
• Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data [89.79617468457393]
  Training models with imbalance rate (class density discrepancy) may lead to suboptimal prediction. We propose a framework for training models for this imbalance issue. We demonstrate our model's improved performance in real-world medical datasets.
  arXiv  Detail & Related papers  (2022-07-23T00:39:53Z)
• BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification [0.0]
  We propose a modified Bootstrapping loss(BS loss) function with Mixup data augmentation strategy. Our experiments indicated that BS loss with Mixup(BSM) model can halve the Expected Error(ECE) compared to standard data augmentation. BSM model is able to perceive the semantic distance of out-of-domain data, demonstrating high potential in real-world clinical practice.
  arXiv  Detail & Related papers  (2022-06-09T13:06:51Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations [2.3204178451683264]
  Variance Aware Training (VAT) method exploits this property by introducing the variance error into the model loss function. We validate VAT on three medical imaging datasets from diverse domains and various learning objectives.
  arXiv  Detail & Related papers  (2021-05-28T21:34:04Z)
• Addressing catastrophic forgetting for medical domain expansion [9.720534481714953]
  Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant decline in performance when tested at other institutions. We develop an approach to address catastrophic forget-ting based on elastic weight consolidation combined with modulation of batch normalization statistics.
  arXiv  Detail & Related papers  (2021-03-24T22:33:38Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• 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.