
Jakub NalepaSilesian University of Technology · Institute of Computer Science
Jakub Nalepa
PhD, DSc
About
260
Publications
38,161
Reads
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3,969
Citations
Introduction
Areas of research:
- machine learning and pattern recognition, especially support vector machines,
- evolutionary algorithms: genetic and memetic algorithms,
- medical imaging and clinical decision support,
- parallel algorithms,
- data mining.
Google Scholar profile:
https://scholar.google.pl/citations?user=kt6EnKcAAAAJ&hl=en
DBLP profile:
http://dblp.uni-trier.de/pers/hd/n/Nalepa:Jakub
Additional affiliations
March 2018 - present
KP Labs
Position
- Head of Department
May 2010 - present
Future Processing
Position
- Senior Researcher
October 2011 - present
Publications
Publications (260)
Despite coronary artery calcium scoring being considered a largely solved problem within the realm of medical artificial intelligence, this paper argues that significant improvements can still be made. By shifting the focus from pathology detection to a deeper understanding of anatomy, the novel algorithm proposed in the paper both achieves high ac...
Enhancing agricultural methods through the utilization of Earth observation and artificial intelligence (AI) has emerged as a significant concern. The ability to quantify soil parameters on a large scale can play a pivotal role in optimizing the fertilization process. While techniques for noninvasive estimation of soil parameters from hyperspectral...
Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum nature presents inherent challenges. One major challenge is the limited resolution of estimated kernel values caused by the finite number of circuit runs performed on a quant...
Monitoring crop irrigation levels and the soil moisture in the root zone is of paramount importance in precision agriculture (PA), as it allows practitioners to optimize the water supply. This can, in turn, directly lead to significant water savings while maintaining appropriate cultivation practices. Currently adopted in-field methods to monitor r...
Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompa...
Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for multivariate time series anomaly detection, especially for the challenging case of satellite telemetry. The European Space Agency B...
Context
The presence of metabolic dysfunction–associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.
Objective
To develop machine learning (ML) models for risk assessment of MASLD occurrence in patients with DM.
Methods
Feature selec...
Accurate and reproducible segmentation of brain tumors from multi-modal magnetic resonance (MR) scans is a pivotal step in practice. In this BraTS Continuous Evaluation initiative, we exploit a 3D nnU-Net for this task which was ranked at the \(6^\textrm{th}\) place (out of 1600 participants) in the BraTS’21 Challenge. We benefit from an ensemble o...
Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms for this task have been proposed for various formulations of rich vehicle routing problems. There is, however, an important gap in the state of the art that concerns the lack of an established and wi...
The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attributed to deep learning, little attention has been given to evaluating the practical value of super-resolved...
Background
Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with...
The AIOPEN project will combine and extend the existing platform framework Automated Service Builder (ASB), Open Interoperable Platform for Unified Access & Analysis of EO Data (EOPEN) and EO Exploitation Platform Common Architecture (EOEPCA) with Artificial Intelligence (AI) and Machine Learning (ML) capabilities.
The resulting platform, AIOPEN, r...
The European Space Agency (ESA) Anomalies Dataset for International AI Anomaly Detection Benchmark is an ongoing project that aims to create a comprehensive largescale dataset of satellite telemetry with curated annotations of anomalies and rare events from 3 different ESA missions (9 years of telemetry in total). The dataset will support the devel...
Background:
For decades, metformin has been the drug of first choice in the management of type 2 diabetes. However, approximately 2–13% of patients do not tolerate metformin due to gastrointestinal (GI) side effects. Since metformin influences the gut microbiota, we hypothesized that a multi-strain probiotics supplementation would mitigate the gas...
The nature of activity in the agricultural sector has changed over the years due to the growth of the population and the increase in its demand for food, fiber and fuel. The limited availability of agricultural land requires targeted management of resource production and leads to the increasing adoption of precision agriculture approaches. This req...
Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may be noisy and...
Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is li...
Localization of a biopsy needle in ultrasound (US) images is an important medical image analysis task, as it may help clinicians reduce the risk of damaging the tissue surrounding the cancer and spreading cancerous cells. Despite numerous studies dedicated to segmenting the needle from US, virtually all of them build upon the strong assumption that...
Aims
As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.
Methods and results
We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27...
Accurate assessment of brain tumor progression from magnetic resonance imaging is a critical issue in clinical practice which allows us to precisely monitor the patient’s response to a given treatment. Manual analysis of such imagery is, however, prone to human errors and lacks reproducibility. Therefore, designing automated end-to-end quantitative...
Accurate and reproducible segmentation of brain tumors from multi-modal magnetic resonance (MR) scans is a pivotal step in clinical practice. In this BraTS Continuous Evaluation initiative, we exploit a 3D nnU-Net for this task which was ranked at the \(6^\textrm{th}\) place (out of 1600 participants) in the BraTS’21 Challenge. We benefit from an e...
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extending classic SVMs with quantum kernels and applying them to satellite data analysis. The design and implementation of SVMs with quantum kernels (hybrid SVMs) is presented....
Satellite telemetry data is a special case of multivariate time series characterized by large volumes (in terms of both the number of series and samples), varying sampling rates (including time gaps), redundant sensors, and many interconnections between the series. Special tools are needed to handle visualization, analysis, and, most importantly, a...
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly expressive and costly circuits to moderate size, task oriented ones. In this work we propose a simple toy model to study the optimization landscape of the Kernel-Target...
Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high...
Detecting anomalies in satellite telemetry data is pivotal in ensuring its safe operations. Although there exist various data-driven techniques for the task of determining abnormal parts of the signal, they are virtually never validated over real telemetries. Analyzing such data is challenging due to its intrinsic characteristics, as telemetry may...
The amount of data generated daily grows tremendously in virtually all domains of science and industry, and its efficient storage, processing and analysis pose significant practical challenges nowadays. To automate the process of extracting useful insights from raw data, numerous supervised machine learning algorithms have been researched so far. T...
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re-scheduling. We approach this...
Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI) bring exciting opportunities to various fields of science and industry that can directly benefit from in-orbit data processing. Taking AI into space may accelerate the response to various events, as massively large raw hyperspectral images (HSIs) can be turned...
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farme...
We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015 - 2020) were analyzed using ML. The occurrence of new CV events following d...
Introduction:
Vitamin D (VD) has a pleiotropic effect on many health aspects yet the results of studies regarding vitamin D deficiency (VDD) and both glycemic control and cardiovascular disease (CVD) are conflicting.
Objective:
To determine the prevalence of VDD and its associations with CVD and glycemic control among patients with type 2 diabet...
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis. The design and implementation of SVMs with quantum kernels (hybrid SVMs) are presented. Here, the pixels are...
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for...
Multispectral Sentinel-2 images are a valuable source of Earth observation data, however spatial resolution of their spectral bands limited to 10 m, 20 m, and 60 m ground sampling distance remains insufficient in many cases. This problem can be addressed with super-resolution, aimed at reconstructing a high-resolution image from a low-resolution ob...
Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them. However, such algorithms must be fed with a huge amount of consistently labeled data during the training process to elaborate a well-generalizing model. In addition, there is a...
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis. The design and implementation of SVMs with quantum kernels (hybrid SVMs) are presented. Here, the pixels are...
Multispectral Sentinel-2 images are a valuable source of Earth observation data, however spatial resolution of their spectral bands limited to 10 m, 20 m, and 60 m ground sampling distance remains insufficient in many cases. This problem can be addressed with super-resolution, aimed at reconstructing a high-resolution image from a low-resolution ob...
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in...
Deep neural networks are powerful learning machines that have laid foundations for most of the recent advancements in data analysis. Their most important advantage lies in learning how to extract the features from raw data, and these deep features are later classified with fully-connected layers. Although there exist more effective classifiers, inc...
Abstract Background Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techn...
Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves...
Hepatic cirrhosis is an increasing cause of mortality in developed countries—it is the pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since cirrhosis evolves from the asymptomatic phase, it is of paramount importance to detect it as quickly as possible, because entering the symptomatic phase commonly leads to ho...
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can help to reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re-scheduling of the clo...