Search results for "Machine learning"
showing 10 items of 1464 documents
SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images.
2021
High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CN…
Przegląd metod śródoperacyjnej oceny marginesów w chirurgicznym leczeniu oszczędzającym gruczoł piersiowy
2021
Breast conserving therapy is the primary treatment modality in early-stage breast cancer patients. Despite the development of methods for the intraoperative assessment of tumor margins, 20–30% of patients still require re-resection due to postoperative tumor infiltration at the surgery margins. In recent years, many methods have been developed to reduce the number of re-resections due to margin infiltration. Here we review the current methods together with several more techniques under investigation.
Novel Approaches for Glioblastoma Treatment: Focus on Tumor Heterogeneity, Treatment Resistance, and Computational Tools
2019
BACKGROUND: Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide (TMZ), a DNA alkylating agent. However, the ability of tumor cells to deeply infiltrate the surrounding tissue makes complete resection quite impossible, and in consequence, the probability of tumor recurrence is high, and the prognosis is not positive. GBM is highly heterogeneous and adapts to treatment in most individuals. Nevertheless, these mechanisms of adaption are unknown. RECENT FINDINGS: In this review, we will discuss the recent discoveries in molecular and cellular heterog…
Improving Communication in Risk Management of Health Information Technology Systems by means of Medical Text Simplification
2019
Health Information Technology Systems (HITS) are increasingly used to improve the quality of patient care while reducing costs. These systems have been developed in response to the changing models of care to an ongoing relationship between patient and care team, supported by the use of technology due to the increased instance of chronic disease. However, the use of HITS may increase the risk to patient safety and security. While standards can be used to address and manage these risks, significant communication problems exist between experts working in different departments. These departments operate in silos often leading to communication breakdowns. For example, risk management stakeholder…
Case-Sensitivity of Classifiers for WSD: Complex Systems Disambiguate Tough Words Better
2007
We present a novel method for improving disambiguation accuracy by building an optimal ensemble (OE) of systems where we predict the best available system for target word using a priori case factors (e.g. amount of training per sense). We report promising results of a series of best-system prediction tests (best prediction accuracy is 0.92) and show that complex/simple systems disambiguate tough/easy words better. The method provides the following benefits: (1) higher disambiguation accuracy for virtually any base systems (current best OE yields close to 2% accuracy gain over Senseval-3 state of the art) and (2) economical way of building more effective ensembles of all types (e.g. optimal,…
OPETH: Open Source Solution for Real-Time Peri-Event Time Histogram Based on Open Ephys
2019
Single cell electrophysiology remains one of the most widely used approaches of systems neuroscience. Decisions made by the experimenter during electrophysiology recording largely determine recording quality, duration of the project and value of the collected data. Therefore, online feedback aiding these decisions can lower monetary and time investment, and substantially speed up projects as well as allow novel studies otherwise not possible due to prohibitively low throughput. Real-time feedback is especially important in studies that involve optogenetic cell type identification by enabling a systematic search for neurons of interest. However, such tools are scarce and limited to costly co…
System-theoretical analysis of the Clare Bishop Area in the cat
1980
The Clare Bishop Area (CBA) is a retinotopically organized cortical area in the cat brain connected to a great variety of visual areas in a very complex wax (Fig. 1). Experimental analysis is difficult because of the following aspects: 1. As the distance from the retina increases, the signal combinations necessary to analyse the system become more and more specific. 2. Feedback loops cannot be opened, so an unequivocal identification of CBA cell properties is impossible. 3. The nonlinear character seems to have a great influence on signal processing. To circumvent these problems, specific signal combinations leading to a separation of input subsystems have been developed (Hoffmann and v. Se…
Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree
2021
Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation p…
Effectiveness of local feature selection in ensemble learning for prediction of antimicrobial resistance
2008
In the real world concepts are often not stable but change over time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift (CD), complicates the task of learning a robust model. Different ensemble learning (EL) approaches (that instead of learning a single classifier try to learn and maintain a set of classifiers over time) have been shown to perform reasonably well in the presence of concept drift. In this paper we study how much local feature selection (FS) can improve ensemble performance for da…
Drug Activity Characterization Using One-Class Support Vector Machines with Counterexamples
2013
The problem of detecting chemical activity in drugs from its molecular description constitutes a challenging and hard learning task. The corresponding prediction problem can be tackled either as a binary classification problem (active versus inactive compounds) or as a one class problem. The first option leads usually to better prediction results when measured over small and fixed databases while the second could potentially lead to a much better characterization of the active class which could be more important in more realistic settings. In this paper, a comparison of these two options is presented when support vector models are used as predictors.