Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images

Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be comparable to the pathologists. This work investigates the capabilities of two DL approaches to assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI BreastPathQ challenge dataset. The effects of training on augmented data via rotations, and combinations of multiple architectures into a single network were analyzed using a modified Kendall Tau-b prediction probability metric known as the average prediction probability PK. A deep, transfer learned, Convolutional Neural Network (CNN) InceptionV3 was used as a baseline, achieving an average PK value of 0.884, showing improvement from the average PK value of 0.83 achieved by pathologists. The network was then trained on additional training datasets which were rotated between 1 and 360 degrees, which saw a peak increase of PK up to 4.2%. An additional architecture consisting of the InceptionV3 network and VGG16, a shallow, transfer learned CNN, was combined in a parallel architecture. This parallel architecture achieved a baseline average PK value of 0.907, a statistically significantly improvement over either of the architectures' performances separately (p<0.0001 by unpaired t-test).Comment: 7 pages (includes a cover page), 5 figures, 1 table, work addresses the BreastPathQ challeng

Hemispheric effects of canonical views of category members with known typicality levels

Is there a preferred hemispheric canonical view of a visual concept? We investigated this question in a natural superordinate category membership decision task using a hemi-field paradigm. Participants had to decide whether or not an image of an object lateralized in the left (LVF) or right (RVF) visual half field is a member of a predesignated superordinate category. The objects represented high, medium, or low typicality levels, and each object had 6 different perspective views (front, front-right, front-left, side, back-left, and back-right). The latency responses revealed a significant interaction of Hemi Field X View X Typicality (there was no hemi-field difference in accuracy). The findings confirm the presence of asymmetry in stored concepts in long-term memory and suggest, in addition, a hemispheric canonical view of these concepts, a view strongly related to typicality level

Rapid prototyping 3D virtual world interfaces within a virtual factory environment

On-going work into user requirements analysis using CLIPS (NASA/JSC) expert systems as an intelligent event simulator has led to research into three-dimensional (3D) interfaces. Previous work involved CLIPS and two-dimensional (2D) models. Integral to this work was the development of the University of Massachusetts Lowell parallel version of CLIPS, called PCLIPS. This allowed us to create both a Software Bus and a group problem-solving environment for expert systems development. By shifting the PCLIPS paradigm to use the VEOS messaging protocol we have merged VEOS (HlTL/Seattle) and CLIPS into a distributed virtual worlds prototyping environment (VCLIPS). VCLIPS uses the VEOS protocol layer to allow multiple experts to cooperate on a single problem. We have begun to look at the control of a virtual factory. In the virtual factory there are actors and objects as found in our Lincoln Logs Factory of the Future project. In this artificial reality architecture there are three VCLIPS entities in action. One entity is responsible for display and user events in the 3D virtual world. Another is responsible for either simulating the virtual factory or communicating with the real factory. The third is a user interface expert. The interface expert maps user input levels, within the current prototype, to control information for the factory. The interface to the virtual factory is based on a camera paradigm. The graphics subsystem generates camera views of the factory on standard X-Window displays. The camera allows for view control and object control. Control or the factory is accomplished by the user reaching into the camera views to perform object interactions. All communication between the separate CLIPS expert systems is done through VEOS

An artificial reality environment for remote factory control and monitoring

Work has begun on the merger of two well known systems, VEOS (HITLab) and CLIPS (NASA). In the recent past, the University of Massachusetts Lowell developed a parallel version of NASA CLIPS, called P-CLIPS. This modification allows users to create smaller expert systems which are able to communicate with each other to jointly solve problems. With the merger of a VEOS message system, PCLIPS-V can now act as a group of entities working within VEOS. To display the 3D virtual world we have been using a graphics package called HOOPS, from Ithaca Software. The artificial reality environment we have set up contains actors and objects as found in our Lincoln Logs Factory of the Future project. The environment allows us to view and control the objects within the virtual world. All communication between the separate CLIPS expert systems is done through VEOS. A graphical renderer generates camera views on X-Windows devices; Head Mounted Devices are not required. This allows more people to make use of this technology. We are experimenting with different types of virtual vehicles to give the user a sense that he or she is actually moving around inside the factory looking ahead through windows and virtual monitors

Initial Results from On-Orbit Testing of the Fram Memory Test Experiment on the Fastsat Micro-Satellite

The Memory Test Experiment is a space test of a ferroelectric memory device on a low Earth orbit satellite that launched in November 2010. The memory device being tested is a commercial Ramtron Inc. 512K memory device. The circuit was designed into the satellite avionics and is not used to control the satellite. The test consists of writing and reading data with the ferroelectric based memory device. Any errors are detected and are stored on board the satellite. The data is sent to the ground through telemetry once a day. Analysis of the data can determine the kind of error that was found and will lead to a better understanding of the effects of space radiation on memory systems. The test is one of the first flight demonstrations of ferroelectric memory in a near polar orbit which allows testing in a varied radiation environment. The initial data from the test is presented. This paper details the goals and purpose of this experiment as well as the development process. The process for analyzing the data to gain the maximum understanding of the performance of the ferroelectric memory device is detailed

How to determine local elastic properties of lipid bilayer membranes from atomic-force-microscope measurements: A theoretical analysis

Measurements with an atomic force microscope (AFM) offer a direct way to probe elastic properties of lipid bilayer membranes locally: provided the underlying stress-strain relation is known, material parameters such as surface tension or bending rigidity may be deduced. In a recent experiment a pore-spanning membrane was poked with an AFM tip, yielding a linear behavior of the force-indentation curves. A theoretical model for this case is presented here which describes these curves in the framework of Helfrich theory. The linear behavior of the measurements is reproduced if one neglects the influence of adhesion between tip and membrane. Including it via an adhesion balance changes the situation significantly: force-distance curves cease to be linear, hysteresis and nonzero detachment forces can show up. The characteristics of this rich scenario are discussed in detail in this article.Comment: 14 pages, 9 figures, REVTeX4 style. New version corresponds to the one accepted by PRE. The result section is restructured: a comparison to experimental findings is included; the discussion on the influence of adhesion between AFM tip and membrane is extende

Results from On-Orbit Testing of the Fram Memory Test Experiment on the Fastsat Micro-Satellite

NASA is planning on going beyond Low Earth orbit with manned exploration missions. The radiation environment for most Low Earth orbit missions is harsher than at the Earth's surface but much less harsh than deep space. Development of new electronics is needed to meet the requirements of high performance, radiation tolerance, and reliability. The need for both Volatile and Non-volatile memory has been identified. Emerging Non-volatile memory technologies (FRAM, C-RAM,M-RAM, R-RAM, Radiation Tolerant FLASH, SONOS, etc.) need to be investigated for use in Space missions. An opportunity arose to fly a small memory experiment on a high inclination satellite (FASTSAT). An off-the-shelf 512K Ramtron FRAM was chosen to be tested in the experiment

Satellite Test of Radiation Impact on Ramtron 512K FRAM

The Memory Test Experiment is a space test of a ferroelectric memory device on a low Earth orbit satellite. The test consists of writing and reading data with a ferroelectric based memory device. Any errors are detected and are stored on board the satellite. The data is send to the ground through telemetry once a day. Analysis of the data can determine the kind of error that was found and will lead to a better understanding of the effects of space radiation on memory systems. The test will be one of the first flight demonstrations of ferroelectric memory in a near polar orbit which allows testing in a varied radiation environment. The memory devices being tested is a Ramtron Inc. 512K memory device. This paper details the goals and purpose of this experiment as well as the development process. The process for analyzing the data to gain the maximum understanding of the performance of the ferroelectric memory device is detailed