Tomas Majtner

Tomas Majtner
Max Planck Institute of Biophysics · Department of Molecular Sociology

PhD

About

30
Publications
9,613
Reads
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405
Citations
Citations since 2016
24 Research Items
373 Citations
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2016201720182019202020212022020406080
Additional affiliations
September 2020 - February 2022
Masaryk University
Position
  • PostDoc Position
September 2018 - August 2020
University of Southern Denmark
Position
  • PostDoc Position
February 2017 - August 2018
Spanish National Research Council
Position
  • PostDoc Position
Education
July 2010 - December 2015
Masaryk University
Field of study
  • Image Processing

Publications

Publications (30)
Article
Full-text available
Background The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widesp...
Preprint
Full-text available
Background: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming wides...
Conference Paper
Full-text available
The paper examines the possibilities of using synthetic HEp-2 cell images as a means of data augmentation. The common problem of biomedical datasets is the shortage of annotated samples required for the training of deep learning techniques. Traditional approaches based on image rotation and mirroring have their limitations, and alternative techniqu...
Article
Full-text available
Background and study aims Small bowel ulcerations are efficiently detected with deep learning techniques, whereas the ability to diagnose Crohnʼs disease (CD) in the colon with it is unknown. This study examined the ability of a deep learning framework to detect CD lesions with pan-enteric capsule endoscopy (CE) and classify lesions of different se...
Conference Paper
Full-text available
In this paper, we examine the effect of texture-based image transformation on classification performance. A novel combination of mathematical morphology operations and contrast-limited adaptive histogram equalization is proposed to enhance image textural features. The suggested operations are applied in HSV colour space, where the intensity compone...
Article
Background and objective: Diabetes mellitus is a common disorder amounting to 400 million patients worldwide. It is often accompanied by a number of complications, including neuropathy, nephropathy, and cardiovascular diseases. For example, peripheral neuropathy is present among 20-30% of diabetics before the diagnosis is substantiated. For this r...
Article
Full-text available
Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythema, is associated with diabetic neuropathy. In clinical practice, facial erythema of patients with diabetes is evaluated based on subjective observations of visible redness, which often goes unnoticed leading to microangiopathic complications. To add...
Article
Full-text available
We propose a novel HEp-2 cell image classifier to improve the automation process of patients' serum evaluation. Our solution builds on the recent progress in deep learning based image classification. We propose an ensemble approach using multiple state-of-the-art architectures. We incorporate additional texture information extracted by an improved...
Article
Full-text available
Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows tha...
Conference Paper
Full-text available
The recognition and classification of medical and biomedical images typically suffer from the problem of a low number of annotated samples. This comes along with the problem of efficient training of the current deep learning frameworks. Therefore, many researchers opt for various techniques which could substitute the traditional training of convolu...
Article
Full-text available
Aim: (1) To quantify the invisible variations of facial erythema that occur as the blood flows in and out of the face of diabetic patients, during the blood pulse wave using an innovative image processing method, on videos recorded with a conventional digital camera and (2) to determine whether this "unveiled" facial red coloration and its periodi...
Article
Full-text available
Motivation: Cryo electron microscopy (EM) is currently one of the main tools to reveal the structural information of biological macromolecules. The re-construction of three-dimensional (3D) maps is typically carried out following an iterative process that requires an initial estimation of the 3D map to be refined in subsequent steps. Therefore, it...
Conference Paper
Full-text available
One of the big challenges in the recognition of biomedical samples is the lack of large annotated datasets. Their relatively small size, when compared to datasets like ImageNet, typically leads to problems with efficient training of current machine learning algorithms. However, the recent development of generative adversarial networks (GANs) appear...
Article
Full-text available
In this article, we are addressing the question of effective usage of the feature set extracted from deep learning models pre-trained on ImageNet. Exploring this option will offer very fast and attractive alternative to transfer learning strategies. The traditional task of skin lesion recognition consists of several stages, where the automated syst...
Article
Single-particle analysis by electron microscopy is a well established technique for analyzing the three-dimensional structures of biological macromolecules. Besides its ability to produce high-resolution structures, it also provides insights into the dynamic behavior of the structures by elucidating their conformational variability. Here, the diffe...
Article
Full-text available
Three dimensional electron microscopy is becoming a very data-intensive field in which vast amounts of experimental images are acquired at high speed. To manage such large-scale projects, we had previously developed a modular workflow system called Scipion (de la Rosa-Trevín et al., 2016). We present here a major extension of Scipion that allows pr...
Article
Full-text available
Electron cryomicroscopy (cryo-EM) is essential for the study and functional understanding of non-crystalline macromolecules such as proteins. These molecules cannot be imaged using X-ray crystallography or other popular methods. Cryo-EM has been successfully used to visualize molecules such as ribosomes, viruses, and ion channels, for example. Obta...
Article
Single Particle Analysis by Electron Microscopy aims at producing a three-dimensional model of a biological macromolecule using projection images acquired with an electron microscope. The task boils down to solving the inverse problem of estimating the three-dimensional structure from thousands of two-dimensional projections of it. The reconstructi...
Preprint
Full-text available
In this report, we are presenting our automated prediction system for disease classification within dermoscopic images. The proposed solution is based on deep learning, where we employed transfer learning strategy on VGG16 and GoogLeNet architectures. The key feature of our solution is preprocessing based primarily on image augmentation and colour...
Article
The introduction of Direct Electron Detector (DED) videos in the Electron Microscope field has boosted Single Particle Analysis to a point in which it is currently considered to be a key technique in Structural Biology. In this article we introduce an approach to estimate the DED camera gain at each pixel from the movies themselves. This gain is ne...
Conference Paper
Full-text available
Melanoma is one of the most lethal forms of skin cancer. It occurs on the skin surface and develops from cells known as melanocytes. The same cells are also responsible for benign lesions commonly known as moles, which are visually similar to melanoma in its early stage. If melanoma is treated correctly, it is very often curable. Currently, much re...
Conference Paper
Full-text available
In dermoscopic images, various thin artefacts naturally appear, most usually in the form of hairs. While trying to find the border of the skin lesion, these artefacts effect the lesion segmentation methods and also the subsequent classification. Currently, there is a lot of research focus in this area and various methods are presented both for skin...
Conference Paper
Full-text available
Melanoma is the most dangerous form of skin cancer. It develops from the melanin-producing cells known as melanocytes. If melanoma is recognized and treated early, it is almost always curable. However, in early stages, melanomas are similar to benign lesions known as moles, which also originate from melanocytes. Therefore, much effort is put on the...
Conference Paper
Full-text available
Classification tasks of biomedical images are still an interesting topic of research with many possibilities of improvement. A very important part in these tasks is the feature extraction, where different image descriptors are used. Recently, a new approach of RSurf features was introduced with application in recognition of the 2D HEp-2 cell images...
Thesis
Full-text available
The design and development of various image processing tools and algorithms for specific scientific as well as diagnostic purposes is still a hot topic. Currently, physicians have easy access to large amount of images from various capturing devices. The analysis of these images, which usually appear in several modalities (CT, MRI, X-ray, etc.),...
Conference Paper
Full-text available
The recognition of patterns with focus on texture and shape analysis is still very hot topic, especially in biomédical image processing. In this article, we introduce 3D extensions of well-known approaches for this particular area. We focus on the collection of MPEG-7 image descriptors, specifically on the Edge Histogram Descriptor (EHD) and Gabor...
Conference Paper
Full-text available
In biomedical image analysis, object description and classification tasks are very common. Our work relates to the problem of classification of Human Epithelial (HEp-2) cells. Since the crucial part of each classification process is the feature extraction and selection, much attention should be concentrated to the development of proper image descri...
Article
Full-text available
Human Epithelial (HEp-2) cells are commonly used in the Indirect Immunofluorescence (IIF) tests to detect autoimmune diseases. The diagnosis consists of searching and classification to specific patterns created by Anti-Nuclear Antibodies (ANAs) in the patient serum. Evaluation of the IIF test is mostly done by humans, which means that it is highly...
Conference Paper
Full-text available
In recent years, research groups pay even more attention on 3D images, especially in the field of biomedical image processing. Adding another dimension enables to capture the entire object. On the other hand, handling 3D images also requires new algorithms, since not all of them can be modified for higher dimensions intuitively. In this article, we...
Conference Paper
Full-text available
The image descriptors are a very useful tool in the task of classification. In biomedical image analysis, they may characterize either the shape or the internal structure of studied objects. Both characteristics are very important. When analysing cells, their shape is usually determined first. In the second step, their mask may be used for the sele...

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Projects

Projects (4)
Project
To detect desired items (like lesions such as colorectal polyps) from digital acquired biomedical images in different modalities.
Project
Cryo EM Integration, Reproducibility and Analysis Scipion is an image processing framework to obtain 3D models of macromolecular complexes using Electron Microscopy.