Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice (Preprint)

2021 ◽  
Author(s):  
Viraj Kulkarni ◽  
Manish Gawali ◽  
Amit Kharat
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Ground Truth ◽  
Research Literature ◽  
Training Data ◽  
Software Developers ◽  
Intelligent Software ◽  
Misclassification Costs ◽  
Clinical Radiology ◽  
Class Representation

UNSTRUCTURED The use of machine learning to develop intelligent software tools for interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. Namely, we discuss: insufficient training data, decentralized datasets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen datasets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify techniques to address it. Although these techniques have been discussed in prior research literature, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders.

scholarly journals Multi-label pathway prediction based on active dataset subsampling

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J. Hallam
Keyword(s):  
Machine Learning ◽  
Microbial Communities ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Training Data ◽  
Great Promise ◽  
Biological Organization ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pathway Prediction

AbstractMachine learning methods show great promise in predicting metabolic pathways at different levels of biological organization. However, several complications remain that can degrade prediction performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of enzymes and pathways within a dataset. This class imbalance problem is commonly encountered by the machine learning community when the proportion of instances over class labels within a dataset are uneven, resulting in poor predictive performance for underrepresented classes. Here, we present leADS, multi-label learning based on active dataset subsampling, that leverages the idea of subsampling points from a pool of data to reduce the negative impact of training loss due to class imbalance. Specifically, leADS performs an iterative process to: (i)-construct an acquisition model in an ensemble framework; (ii) select informative points using an appropriate acquisition function; and (iii) train on selected samples. Multiple base learners are implemented in parallel where each is assigned a portion of labeled training data to learn pathways. We benchmark leADS using a corpora of 10 experimental datasets manifesting diverse multi-label properties used in previous pathway prediction studies, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previously reported machine learning methods for both organismal and multi-organismal genomes while establishing an extensible framework for navigating class imbalances across diverse real world datasets.Availability and implementationThe software package, and installation instructions are published on github.com/[email protected]

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

Glean

2021 ◽  
Vol 14 (6) ◽  
pp. 997-1005
Author(s):  
Sandeep Tata ◽  
Navneet Potti ◽  
James B. Wendt ◽  
Lauro Beltrão Costa ◽  
Marc Najork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Real World ◽  
Empirical Studies ◽  
Ground Truth ◽  
Training Data ◽  
Ground Truth Data ◽  
Document Type ◽  
Machine Learning Model ◽  
Structured Information

Extracting structured information from templatic documents is an important problem with the potential to automate many real-world business workflows such as payment, procurement, and payroll. The core challenge is that such documents can be laid out in virtually infinitely different ways. A good solution to this problem is one that generalizes well not only to known templates such as invoices from a known vendor, but also to unseen ones. We developed a system called Glean to tackle this problem. Given a target schema for a document type and some labeled documents of that type, Glean uses machine learning to automatically extract structured information from other documents of that type. In this paper, we describe the overall architecture of Glean, and discuss three key data management challenges : 1) managing the quality of ground truth data, 2) generating training data for the machine learning model using labeled documents, and 3) building tools that help a developer rapidly build and improve a model for a given document type. Through empirical studies on a real-world dataset, we show that these data management techniques allow us to train a model that is over 5 F1 points better than the exact same model architecture without the techniques we describe. We argue that for such information-extraction problems, designing abstractions that carefully manage the training data is at least as important as choosing a good model architecture.

Programmatic Labeling of Dark Data for Artificial Intelligence in Spatial Informatics

2021 ◽  
Author(s):  
Jason Meil
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Joint Probability ◽  
Ground Truth ◽  
Training Data ◽  
Data Sources ◽  
Joint Probability Distribution ◽  
Weak Supervision ◽  
Response Variable ◽  
High Degree

<p>Data preparation process generally consumes up to 80% of the Data Scientists time, with 60% of that being attributed to cleaning and labeling data.[1]  Our solution is to use automated pipelines to prepare, annotate, and catalog data. The first step upon ingestion, especially in the case of real world—unstructured and unlabeled datasets—is to leverage Snorkel, a tool specifically designed around a paradigm to rapidly create, manage, and model training data. Configured properly, Snorkel can be leveraged to temper this labeling bottle-neck through a process called weak supervision. Weak supervision uses programmatic labeling functions—heuristics, distant supervision, SME or knowledge base—scripted in python to generate “noisy labels”. The function traverses the entirety of the dataset and feeds the labeled data into a generative—conditionally probabilistic—model. The function of this model is to output the distribution of each response variable and predict the conditional probability based on a joint probability distribution algorithm. This is done by comparing the various labeling functions and the degree to which their outputs are congruent to each other. A single labeling function that has a high degree of congruence with other labeling functions will have a high degree of learned accuracy, that is, the fraction of predictions that the model got right. Conversely, single labeling functions that have a low degree of congruence with other functions will have low learned accuracy. Each prediction is then combined by the estimated weighted accuracy, whereby the predictions of the higher learned functions are counted multiple times. The result yields a transformation from a binary classification of 0 or 1 to a fuzzy label between 0 and 1— there is “x” probability that based on heuristic “n”, the response variable is “y”. The addition of data to this generative model multi-class inference will be made on the response variables positive, negative, or abstain, assigning probabilistic labels to potentially millions of data points. Thus, we have generated a discriminative ground truth for all further labeling efforts and have improved the scalability of our models. Labeling functions can be applied to unlabeled data to further machine learning efforts.<br> <br>Once our datasets are labeled and a ground truth is established, we need to persist the data into our delta lake since it combines the most performant aspects of a warehouse with the low-cost storage for data lakes. In addition, the lake can accept unstructured, semi structured, or structured data sources, and those sources can be further aggregated into raw ingestion, cleaned, and feature engineered data layers.  By sectioning off the data sources into these “layers”, the data engineering portion is abstracted away from the data scientist, who can access model ready data at any time.  Data can be ingested via batch or stream. <br> <br>The design of the entire ecosystem is to eliminate as much technical debt in machine learning paradigms as possible in terms of configuration, data collection, verification, governance, extraction, analytics, process management, resource management, infrastructure, monitoring, and post verification. </p>

scholarly journals Metrics for Automatic Evaluation of Text from NLP Models for Text to Scene Generation

2021 ◽  
Vol 5 (4) ◽  
pp. 20-25
Author(s):  
S. Yashaswini ◽  
S. S. Shylaja
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cost Function ◽  
Performance Metrics ◽  
Evaluation Criteria ◽  
Ground Truth ◽  
Automatic Evaluation ◽  
Mean Square ◽  
Learning Models ◽  
Classification Tasks

Performance metrics give us an indication of which model is better for which task. Researchers attempt to apply machine learning and deep learning models to measure the performance of models through cost function or evaluation criteria like Mean square error (MSE) for regression, accuracy, and f1-score for classification tasks Whereas in NLP performance measurement is a complex due variation of ground truth and results obta.

scholarly journals The NK Landscape as a Versatile Benchmark for Machine Learning Driven Protein Engineering

2020 ◽  
Author(s):  
Adam C. Mater ◽  
Mahakaran Sandhu ◽  
Colin Jackson
Keyword(s):  
Machine Learning ◽  
Protein Engineering ◽  
Performance Metrics ◽  
Training Data ◽  
Rigorous Evaluation ◽  
Accurate Evaluation

AbstractMachine learning (ML) has the potential to revolutionize protein engineering. However, the field currently lacks standardized and rigorous evaluation benchmarks for sequence-fitness prediction, which makes accurate evaluation of the performance of different architectures difficult. Here we propose a unifying framework for ML-driven sequence-fitness prediction. Using simulated (the NK model) and empirical sequence landscapes, we define four key performance metrics: interpolation within the training domain, extrapolation outside the training domain, robustness to sparse training data, and ability to cope with epistasis/ruggedness. We show that architectural differences between algorithms consistently affect performance against these metrics across both experimental and theoretical landscapes. Moreover, landscape ruggedness is revealed to be the greatest determinant of the accuracy of sequence-fitness prediction. We hope that this benchmarking method and the code that accompanies it will enable robust evaluation and comparison of novel architectures in this emerging field and assist in the adoption of ML for protein engineering.

scholarly journals Genus-Physiognomy-Ecosystem Map with 88 Legends Produced at 10m-resolution First-time in a Country Scale through Machine Learning of Multi-temporal Satellite Images

2022 ◽  
Author(s):  
Ram C. Sharma ◽  
Keitarou Hara
Keyword(s):  
Machine Learning ◽  
Satellite Images ◽  
Graphics Processing Unit ◽  
Ground Truth ◽  
Training Data ◽  
Gradient Boosting ◽  
Processing Unit ◽  
Spectral Bands ◽  
Multi Temporal ◽  
First Time

This research introduces Genus-Physiognomy-Ecosystem (GPE) mapping at a prefecture level through machine learning of multi-spectral and multi-temporal satellite images at 10m spatial resolution, and later integration of prefecture wise maps into country scale for dealing with 88 GPE types to be classified from a large size of training data involved in the research effectively. This research was made possible by harnessing entire archives of Level-2A product, Bottom of Atmosphere reflectance images collected by MultiSpectral Instruments onboard a constellation of two polar-orbiting Sentinel-2 mission satellites. The satellite images were pre-processed for cloud masking and monthly median composite images consisting of 10 multi-spectral bands and 7 spectral indexes were generated. The ground truth labels were extracted from extant vegetation survey maps by implementing systematic stratified sampling approach and noisy labels were dropped out for preparing a reliable ground truth database. Graphics Processing Unit (GPU) implementation of Gradient Boosting Decision Trees (GBDT) classifier was employed for classification of 88 GPE types from 204 satellite features. The classification accuracy computed with 25% test data varied from 65-81% in terms of F1-score across 48 prefectural regions. This research produced seamless maps of 88 GPE types first time at a country scale with an average 72% F1-score.

scholarly journals Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

2018 ◽  
Author(s):  
Naihui Zhou ◽  
Zachary D Siegel ◽  
Scott Zarecor ◽  
Nigel Lee ◽  
Darwin A Campbell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Best Practices ◽  
Ground Truth ◽  
Training Data ◽  
Quality Data ◽  
High Quality ◽  
Ground Truth Data ◽  
Plant Phenomics

AbstractThe accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.Author SummaryFood security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, butthey require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel – the male flower of the corn plant – from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts.

scholarly journals Training data augmentation for deep learning radio frequency systems

2021 ◽  
pp. 154851292199124
Author(s):  
William H Clark ◽  
Steven Hauser ◽  
William C Headley ◽  
Alan J Michaels
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Radio Frequency ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Training Data ◽  
Peak Performance ◽  
Modulation Classification ◽  
Data Types ◽  
Significant Information

Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the category of neural networks, and deep learning specifically, the first two are the architecture for the model being trained and the training approach used. This work focuses on the third component, the data used during training. The primary questions that arise are “what is in the data” and “what within the data matters?” looking into the radio frequency machine learning (RFML) field of automatic modulation classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels. Three questions are discussed within this work: (1) how useful a synthetically trained system is expected to be when deployed without considering the environment within the synthesis, (2) how can augmentation be leveraged within the RFML domain, and, lastly, (3) what impact knowledge of degradations to the signal caused by the transmission channel contributes to the performance of a system. In general, the examined data types each make useful contributions to a final application, but captured data germane to the intended use case will always provide more significant information and enable the greatest performance. Despite the benefit of captured data, the difficulties and costs that arise from live collection often make the quantity of data needed to achieve peak performance impractical. This paper helps quantify the balance between real and synthetic data, offering concrete examples where training data is parametrically varied in size and source.

scholarly journals A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

2021 ◽  
Vol 502 (2) ◽  
pp. 2770-2786
Author(s):  
S Mucesh ◽  
W G Hartley ◽  
A Palmese ◽  
O Lahav ◽  
L Whiteway ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Galaxy Evolution ◽  
Performance Metrics ◽  
Stellar Mass ◽  
Distribution Functions ◽  
Training Data ◽  
Computer Hardware ◽  
Basic Set ◽  
Template Fitting

ABSTRACT We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro  1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

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