“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

International Journal of Computer and Applications

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Artificial intelligence in radiology

Compare your culture to one of the cultures discussed in this unit. On a sheet of paper, list the cultures you are comparing and make one column titled “similarities,” and a second column titled “differences.” Now, list as many similarities and differences between the two as you can think of. Are there more similarities or differences between the two cultures you selected? Have you ever met anyone from this culture? How can you use this information to build greater respect between cultures?

Similarities and Differences

Orthogonal frequency division multiplexing (OFDM) has been adopted as a standard for many modern wireless applications requiring high data rate due to bandwidth efficiency, resistance to frequency selective fading and simple digital realization using IFFT/FFT operations. However, physical implementation of the OFDM system suffers from several difficulties. One of the major limitations of OFDM is that it suffers from high peak-to-average power ratio (PAPR), which results in inter-carrier interference (ICI), high out-ofband radiation, and degradation of bit error rate performance. In this paper, different OFDM PAPR reduction techniques are reviewed and analyzed based on their computational complexity, bandwidth requirement and error performance.

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Peak-to-Average Power Ratio Reduction Techniques for OFDM Signals

In this study, we have developed a wireless, portable, standalone, and simple electric cell-substrate impedance sensing (ECIS) system to analyze in-depth functional aspects of cellular functions on the surface of a co-planar metal electrode coated on conventional glass substrate using a low-cost circuitry and correlated it with an equivalent electrical circuit (EEC) model. Low-cost circuitry was used for studying the dynamic behavior of the mouse myoblast cells (C2C12) in a culture chamber. Further, the developed ECIS system was connected with 8-bit Arduino UNO microcontroller board for establishing a compact sized measuring unit, which can be placed inside a CO2 incubator to provide proper environmental condition for the biological cells during the entire measuring time. Integrating ZigBee RF module with the 8-bit Arduino UNO microcontroller board provides a wireless communication network. Theoretical calculation of the lumped-elemental electrical parameters associated with cell-electrolyte interface and metal-electrolyte interface was calculated. The calculation was performed by fitting the experimental impedance data to EEC model using least mean square method to determine the dynamic and vital functions of the mammalian cells such as proliferation (in real-time) with a change in intrinsic electrical parameters associated at any particular time point. Impedance measurements and the lumped-elemental electrical parameter were correlated with the respective microscopic images. The developed ECIS system was found to enable measuring of the extent of cellular proliferation over time. The compactness of the developed ECIS system integrated with the ZigBee RF module and the 8-bit Arduino UNO microcontroller board facilitates its utilization even when placed in the CO2 incubator for a prolonged time.

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A portable standalone wireless electric cell-substrate impedance sensing (ECIS) system for assessing dynamic behavior of mammalian cells

Colon – Targeted Drug Delivery System: A Review

Image Segmentation is the key process in medical imaging which produces different ROI (Region of Interests) as per needed by the medical practitioner, who uses it for further treatment planning. Fast processing of the medical image is a major requirement in today's scenario. In the case of abdominal CT/MR Scans images due to the dimension and the presence of multiple regions like liver, kidney, spleen etc fast processing is very much required. This paper proposed a method for kidney segmentation from an abdominal image and it suggest the idea about segmentation of multiple regions like Spine, Kidney, Liver of Abdominal image in order to produce efficient and prompt result. All the activated cores of a particular processor are responsible to handle their individual region. The main approach of the proposed system is the allocation of spine as a land mark for coordinate reference in a particular core. This is more reliable to identify kidney region seed point identification while moving towards on the axis of centroid of spine. Kidneys region can be easily identify on different cores for faster execution. A new core can be activated for liver segmentation for this we have used prior knowledge of the image. This proposed method is the efficient way to assign particular abdominal region to particular core in available Multicore Architecture to execute speedy and efficient result rather than the existing parallel implementation. Proposed method demonstrate that the parallel implementation of region wise segmentation of abdominal CT/MR image results in a very good speed-up compared with sequential implementation on a core i3 processor and Intel Xeon Processor.

An intelligent system for segmenting an abdominal image in multi core architecture

/pdf/an-overview-of-5g-technology-3hs6gnt03c.pdf

Neeraj Sharma

Papers

Peak-to-Average Power Ratio Reduction Techniques for OFDM Signals

A portable standalone wireless electric cell-substrate impedance sensing (ECIS) system for assessing dynamic behavior of mammalian cells

Colon – Targeted Drug Delivery System: A Review

An intelligent system for segmenting an abdominal image in multi core architecture

An Overview of 5G Technology