2,102 research outputs found
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Class Activation Mapping and Uncertainty Estimation in Multi-Organ Segmentation
Deep learning (DL)-based medical imaging and image segmentation algorithms achieve impressive performance on many benchmarks. Yet the efficacy of deep learning methods for future clinical applications may become questionable due to the lack of ability to reason with uncertainty and interpret probable areas of failures in prediction decisions. Therefore, it is desired that such a deep learning model for segmentation classification is able to reliably predict its confidence measure and map back to the original imaging cases to interpret the prediction decisions. In this work, uncertainty estimation for multiorgan segmentation task is evaluated to interpret the predictive modeling in DL solutions. We use the state-of-the-art nnU-Net to perform segmentation of 15 abdominal organs (spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, right adrenal gland, left adrenal gland, duodenum, bladder, prostate/uterus) using 200 patient cases for the Multimodality Abdominal Multi-Organ Segmentation Challenge 2022. Further, the softmax probabilities from different variants of nnU-Net are used to compute the knowledge uncertainty in the deep learning framework. Knowledge uncertainty from ensemble of DL models is utilized to quantify and visualize class activation map for two example segmented organs. The preliminary result of our model shows that class activation maps may be used to interpret the prediction decision made by the DL model used in this study
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Dropout has been commonly used to quantify prediction uncertainty, i.e, the
variations of model predictions on a given input example. However, using
dropout in practice can be expensive as it requires running dropout inferences
many times.
In this paper, we study how to estimate dropout prediction uncertainty in a
resource-efficient manner. We demonstrate that we can use neuron activation
strengths to estimate dropout prediction uncertainty under different dropout
settings and on a variety of tasks using three large datasets, MovieLens,
Criteo, and EMNIST. Our approach provides an inference-once method to estimate
dropout prediction uncertainty as a cheap auxiliary task. We also demonstrate
that using activation features from a subset of the neural network layers can
be sufficient to achieve uncertainty estimation performance almost comparable
to that of using activation features from all layers, thus reducing resources
even further for uncertainty estimation.Comment: 8 page
Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
Uncertainty estimation in Neural Networks (NNs) is vital in improving
reliability and confidence in predictions, particularly in safety-critical
applications. Bayesian Neural Networks (BayNNs) with Dropout as an
approximation offer a systematic approach to quantifying uncertainty, but they
inherently suffer from high hardware overhead in terms of power, memory, and
computation. Thus, the applicability of BayNNs to edge devices with limited
resources or to high-performance applications is challenging. Some of the
inherent costs of BayNNs can be reduced by accelerating them in hardware on a
Computation-In-Memory (CIM) architecture with spintronic memories and
binarizing their parameters. However, numerous stochastic units are required to
implement conventional dropout-based BayNN. In this paper, we propose the Scale
Dropout, a novel regularization technique for Binary Neural Networks (BNNs),
and Monte Carlo-Scale Dropout (MC-Scale Dropout)-based BayNNs for efficient
uncertainty estimation. Our approach requires only one stochastic unit for the
entire model, irrespective of the model size, leading to a highly scalable
Bayesian NN. Furthermore, we introduce a novel Spintronic memory-based CIM
architecture for the proposed BayNN that achieves more than energy
savings compared to the state-of-the-art. We validated our method to show up to
a improvement in predictive performance and superior uncertainty
estimates compared to related works
Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology
A model's ability to express its own predictive uncertainty is an essential
attribute for maintaining clinical user confidence as computational biomarkers
are deployed into real-world medical settings. In the domain of cancer digital
histopathology, we describe a novel, clinically-oriented approach to
uncertainty quantification (UQ) for whole-slide images, estimating uncertainty
using dropout and calculating thresholds on training data to establish cutoffs
for low- and high-confidence predictions. We train models to identify lung
adenocarcinoma vs. squamous cell carcinoma and show that high-confidence
predictions outperform predictions without UQ, in both cross-validation and
testing on two large external datasets spanning multiple institutions. Our
testing strategy closely approximates real-world application, with predictions
generated on unsupervised, unannotated slides using predetermined thresholds.
Furthermore, we show that UQ thresholding remains reliable in the setting of
domain shift, with accurate high-confidence predictions of adenocarcinoma vs.
squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts
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