scholarly journals A Whole-Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

2021 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Christoph Neuner ◽  
Roland Coras ◽  
Ingmar Blümcke ◽  
Alexander Popp ◽  
Sven M. Schlaffer ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Relevant Information ◽  
Classification Problem ◽  
Use Cases ◽  
Dysembryoplastic Neuroepithelial Tumor ◽  
Low Grade ◽  
Use Case ◽  
Corticotroph Adenoma ◽  
Silent Corticotroph Adenoma ◽  
Second Use

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and labor intensive. We developed an open-source library dealing with recurrent tasks in the processing of WSI and helping with the training and evaluation of neuronal networks for classification tasks. Methods: Two histopathology use-cases were selected and only hematoxylin and eosin (H&E) stained slides were used. The first use case was a two-class classification problem. We trained a convolutional neuronal network (CNN) to distinguish between dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG), two neuropathological low-grade epilepsy-associated tumor entities. Within the second use case, we included four clinicopathological disease conditions in a multilabel approach. Here we trained a CNN to predict the hormone expression profile of pituitary adenomas. In the same approach, we also predicted clinically silent corticotroph adenoma. Results: Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. For the second use case, the best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma, and 0.98 for gonadotroph adenoma. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive and fastai-compatible library is helpful to standardize the workflow and minimize the burden of training a CNN. Indeed, our trained CNNs extracted neuropathologically relevant information from the WSI. This approach will supplement the clinicopathological diagnosis of brain tumors, which is currently based on cost-intensive microscopic examination and variable panels of immunohistochemical stainings.

scholarly journals A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

2021 ◽  
Author(s):  
Christoph Neuner ◽  
Roland Coras ◽  
Ingmar Blümcke ◽  
Alexander Popp ◽  
Sven M. Schlaffer ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Relevant Information ◽  
Use Cases ◽  
Dysembryoplastic Neuroepithelial Tumor ◽  
Low Grade ◽  
Corticotroph Adenoma ◽  
Slide Image ◽  
Silent Corticotroph Adenoma ◽  
Hematoxylin And Eosin ◽  
Pathological Disease

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma and 0.98 for gonadotroph adenoma. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

scholarly journals A Whole Slide Image Managing Library Based on Fastai for Deep Learning in the Context of Histopathology: Two Use-Cases Explained

2021 ◽  
Author(s):  
Christoph Neuner ◽  
Roland Coras ◽  
Ingmar Blümcke ◽  
Alexander Popp ◽  
Sven M. Schlaffer ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Relevant Information ◽  
Use Cases ◽  
Dysembryoplastic Neuroepithelial Tumor ◽  
Low Grade ◽  
Corticotroph Adenoma ◽  
Slide Image ◽  
Silent Corticotroph Adenoma ◽  
Hematoxylin And Eosin ◽  
Pathological Disease

Background: Processing whole-slide images (WSI) to train neural networks can be intricate and laborious. We developed an open-source library covering recurrent tasks in processing of WSI and in evaluating the performance of the trained networks for classification tasks. Methods: Two histopathology use-cases were selected. First we aimed to train a CNN to distinguish H&E-stained slides obtained from neuropathologically classified low-grade epilepsy-associated dysembryoplastic neuroepithelial tumor (DNET) and ganglioglioma (GG). The second project we trained a convolutional neural network (CNN) to predict the hormone expression of pituitary adenoms only from hematoxylin and eosin (H&E) stained slides. In the same approach, we addressed the issue to also predict clinically silent corticotroph adenoma. We included four clinico-pathological disease conditions in a multilabel approach. Results: Our best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma and 0.98 for gonadotroph adenoma. Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive library is most helpful to standardize the work-flow and minimize the work-burden in training CNN. It is also compatible with fastai. Indeed, our new CNNs reliably extracted neuropathologically relevant information from the H&E staining only. This approach will supplement the clinico-pathological diagnosis of brain tumors, which is currently based on cost-intense microscopic examination and variable panels of immunohistochemical stainings.

scholarly journals Clustering with Scikit-Learn in Python

2021 ◽  
Author(s):  
Thomas Jurczyk
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Clustering Algorithms ◽  
Use Cases ◽  
Use Case ◽  
Greco Roman ◽  
Textual Data ◽  
Exploratory Data ◽  
Second Use

This tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis.

Visual Support for Use Case Modeling

2011 ◽  
pp. 1-19
Author(s):  
Andrew Gemino ◽  
Drew Parker
Keyword(s):  
Problem Solving ◽  
Systems Analysis ◽  
Visual Cues ◽  
Cognitive Theory ◽  
Unified Modeling Language ◽  
Relevant Information ◽  
Use Cases ◽  
Use Case ◽  
Visual Support ◽  
Level Of Understanding

The Unified Modeling Language (UML) has been evolving as a standard approach to Systems Analysis and Design. Use cases are a de facto standard tool, and corresponding use case diagrams offer visual support for this tool. The Cognitive Theory of Multimedia Learning suggests that the visual nature of use case diagrams would enhance understanding, particularly for novice users, by providing visual cues to focus relevant information. This paper describes an experiment to test this theory, offering use cases with and without supporting use case diagrams. Retention, comprehension, and problem solving tasks were tested and measured. As hypothesized, the results find that users had a significantly higher level of understanding, measured by problem solving tasks, if they were provided with use case diagrams accompanying the use cases. These results are promising support that use cases and use case diagrams could be considered important boundary objects in systems analysis.

Parquet Cube to store and process gridded data

2020 ◽  
Author(s):  
elisabeth lambert ◽  
Jean-michel Zigna ◽  
Thomas Zilio ◽  
Flavien Gouillon
Keyword(s):  
Big Data ◽  
Open Source ◽  
Value Chain ◽  
Environmental Data ◽  
Use Cases ◽  
Use Case ◽  
Gridded Data ◽  
The Earth ◽  
Oceanographic Data ◽  
Second Use

<p>The volume of data in the earth data observation domain grows considerably, especially with the emergence of new generations of satellites providing much more precise measures and thus voluminous data and files. The ‘big data’ field provides solutions for storing and processing huge amount of data. However, there is no established consensus, neither in the industrial market nor the open source community, on big data solutions adapted to the earth data observation domain. The main difficulty is that these multi-dimensional data are not naturally scalable. CNES and CLS, driven by a CLS business needs, carried out a study to address this difficulty and try to answer it.</p><p>Two use cases have been identified, these two being complementary because at different points in the value chain: 1) the development of an altimetric processing chain, storing low level altimetric measurements from multiple satellite missions, and 2) the extraction of oceanographic environmental data along animal and ships tracks. The original data format of these environmental variables is netCDF. We will first show the state of the art of big data technologies that are adapted to this problematic and their limitations. Then, we will describe the prototypes behind both use cases and in particular how the data is split into independent chunks that then can be processed in parallel. The storage format chosen is the Apache parquet and in the first use case, the manipulation of the data is made with the xarray library while all the parallel processes are implemented with the Dask framework. An implementation using Zarr library instead of Parquet has also been developed and results will also be shown. In the second use case, the enrichment of the track with METOC (Meteo/Oceanographic) data is developed using the Spark framework. Finally, results of this second use case, that runs operationally today for the extraction of oceanographic data along tracks, will be shown. This second solution is an alternative to Pangeo solution in the world of industrial and Java development. It extends the traditional THREDDS subsetter, delivered by the Open source Unidata Community, to a bigdata implementation. This Parquet storage and associated service implements a smoothed transition of gridded data in Big Data infrastructures.</p>

scholarly journals UnrealHaptics: Plugins for Advanced VR Interactions in Modern Game Engines

2021 ◽  
Vol 2 ◽  
Author(s):  
Janis Rosskamp ◽  
Hermann Meißenhelter ◽  
Rene Weller ◽  
Marc O. Rüdel ◽  
Johannes Ganser ◽  
...  
Keyword(s):  
Collision Detection ◽  
State Of The Art ◽  
Haptic Rendering ◽  
Use Cases ◽  
Use Case ◽  
The Core ◽  
Multiple Contacts ◽  
Game Engines ◽  
Virtual Grasping ◽  
Second Use

We present UnrealHaptics, a plugin-architecture that enables advanced virtual reality (VR) interactions, such as haptics or grasping in modern game engines. The core is a combination of a state-of-the-art collision detection library with support for very fast and stable force and torque computations and a general device plugin for communication with different input/output hardware devices, such as haptic devices or Cybergloves. Our modular and lightweight architecture makes it easy for other researchers to adapt our plugins to their requirements. We prove the versatility of our plugin architecture by providing two use cases implemented in the Unreal Engine 4 (UE4). In the first use case, we have tested our plugin with a haptic device in different test scenes. For the second use case, we show a virtual hand grasping an object with precise collision detection and handling multiple contacts. We have evaluated the performance in our use cases. The results show that our plugin easily meets the requirements of stable force rendering at 1 kHz for haptic rendering even in highly non-convex scenes, and it can handle the complex contact scenarios of virtual grasping.

Silent corticotroph adenoma: Experience with six cases

2020 ◽  
Author(s):  
Soler Guillermo Serra ◽  
Barceló Carlos Antich ◽  
Cubas Javier Bodoque ◽  
Fernández Honorato García ◽  
Bonet Antonio Mas ◽  
...  
Keyword(s):  
Corticotroph Adenoma ◽  
Silent Corticotroph Adenoma

scholarly journals Using Actors and Use Cases for Software Size Estimation

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 592
Author(s):  
Radek Silhavy ◽  
Petr Silhavy ◽  
Zdenka Prokopova
Keyword(s):  
Project Planning ◽  
Use Cases ◽  
New Method ◽  
Software Systems ◽  
Software Project ◽  
Use Case ◽  
Size Estimation ◽  
Software Size Estimation ◽  
Use Case Points

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based methods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method.

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