Applications of Sequential Learning for Medical Image Classification

Purpose: The aim of this work is to develop a neural network training framework for continual training of small amounts of medical imaging data and create heuristics to assess training in the absence of a hold-out validation or test set. Materials and Methods: We formulated a retrospective sequential learning approach that would train and consistently update a model on mini-batches of medical images over time. We address problems that impede sequential learning such as overfitting, catastrophic forgetting, and concept drift through PyTorch convolutional neural networks (CNN) and publicly available Medical MNIST and NIH Chest X-Ray imaging datasets. We begin by comparing two methods for a sequentially trained CNN with and without base pre-training. We then transition to two methods of unique training and validation data recruitment to estimate full information extraction without overfitting. Lastly, we consider an example of real-life data that shows how our approach would see mainstream research implementation. Results: For the first experiment, both approaches successfully reach a ~95% accuracy threshold, although the short pre-training step enables sequential accuracy to plateau in fewer steps. The second experiment comparing two methods showed better performance with the second method which crosses the ~90% accuracy threshold much sooner. The final experiment showed a slight advantage with a pre-training step that allows the CNN to cross ~60% threshold much sooner than without pre-training. Conclusion: We have displayed sequential learning as a serviceable multi-classification technique statistically comparable to traditional CNNs that can acquire data in small increments feasible for clinically realistic scenarios

Evidential Uncertainty Quantification: A Variance-Based Perspective

Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advances in evidential deep learning shed light on the direct quantification of aleatoric and epistemic uncertainties with a single forward pass of the model. Most traditional approaches adopt an entropy-based method to derive evidential uncertainty in classification, quantifying uncertainty at the sample level. However, the variance-based method that has been widely applied in regression problems is seldom used in the classification setting. In this work, we adapt the variance-based approach from regression to classification, quantifying classification uncertainty at the class level. The variance decomposition technique in regression is extended to class covariance decomposition in classification based on the law of total covariance, and the class correlation is also derived from the covariance. Experiments on cross-domain datasets are conducted to illustrate that the variance-based approach not only results in similar accuracy as the entropy-based one in active domain adaptation but also brings information about class-wise uncertainties as well as between-class correlations. The code is available at https://github.com/KerryDRX/EvidentialADA. This alternative means of evidential uncertainty quantification will give researchers more options when class uncertainties and correlations are important in their applications.Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 202

Imaging of Glial Cell Activation and White Matter Integrity in Brains of Active and Recently Retired National Football League Players

Importance: Microglia, the resident immune cells of the central nervous system, play an important role in the brain\u27s response to injury and neurodegenerative processes. It has been proposed that prolonged microglial activation occurs after single and repeated traumatic brain injury, possibly through sports-related concussive and subconcussive injuries. Limited in vivo brain imaging studies months to years after individuals experience a single moderate to severe traumatic brain injury suggest widespread persistent microglial activation, but there has been little study of persistent glial cell activity in brains of athletes with sports-related traumatic brain injury. Objective: To measure translocator protein 18 kDa (TSPO), a marker of activated glial cell response, in a cohort of National Football League (NFL) players and control participants, and to report measures of white matter integrity. Design, Setting, and Participants: This cross-sectional, case-control study included young active (n = 4) or former (n = 10) NFL players recruited from across the United States, and 16 age-, sex-, highest educational level-, and body mass index-matched control participants. This study was conducted at an academic research institution in Baltimore, Maryland, from January 29, 2015, to February 18, 2016. Main Outcomes and Measures: Positron emission tomography-based regional measures of TSPO using [11C]DPA-713, diffusion tensor imaging measures of regional white matter integrity, regional volumes on structural magnetic resonance imaging, and neuropsychological performance. Results: The mean (SD) ages of the 14 NFL participants and 16 control participants were 31.3 (6.1) years and 27.6 (4.9) years, respectively. Players reported a mean (SD) of 7.0 (6.4) years (range, 1-21 years) since the last self-reported concussion. Using [11C]DPA-713 positron emission tomographic data from 12 active or former NFL players and 11 matched control participants, the NFL players showed higher total distribution volume in 8 of the 12 brain regions examined (P \u3c .004). We also observed limited change in white matter fractional anisotropy and mean diffusivity in 13 players compared with 15 control participants. In contrast, these young players did not differ from control participants in regional brain volumes or in neuropsychological performance. Conclusions and Relevance: The results suggest that localized brain injury and repair, indicated by higher TSPO signal and white matter changes, may be associated with NFL play. Further study is needed to confirm these findings and to determine whether TSPO signal and white matter changes in young NFL athletes are related to later onset of neuropsychiatric symptoms

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing