77 research outputs found
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
Among all the sub-sections in a typical radiology report, the Clinical
Indications, Findings, and Impression often reflect important details about the
health status of a patient. The information included in Impression is also
often covered in Findings. While Findings and Impression can be deduced by
inspecting the image, Clinical Indications often require additional context.
The cognitive task of interpreting medical images remains the most critical and
often time-consuming step in the radiology workflow. Instead of generating an
end-to-end radiology report, in this paper, we focus on generating the Findings
from automated interpretation of medical images, specifically chest X-rays
(CXRs). Thus, this work focuses on reducing the workload of radiologists who
spend most of their time either writing or narrating the Findings. Unlike past
research, which addresses radiology report generation as a single-step image
captioning task, we have further taken into consideration the complexity of
interpreting CXR images and propose a two-step approach: (a) detecting the
regions with abnormalities in the image, and (b) generating relevant text for
regions with abnormalities by employing a generative large language model
(LLM). This two-step approach introduces a layer of interpretability and aligns
the framework with the systematic reasoning that radiologists use when
reviewing a CXR.Comment: Information Technology and Quantitative Management (ITQM 2023
NASA Tech Briefs, December 1989
Topics include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, and Life Sciences
Triathlon of Lightweight Block Ciphers for the Internet of Things
In this paper, we introduce a framework for the benchmarking of lightweight block ciphers on a multitude of embedded platforms. Our framework is able to evaluate the execution time, RAM footprint, as well as binary code size, and allows one to define a custom "figure of merit" according to which all evaluated candidates can be ranked. We used the framework to benchmark implementations of 19 lightweight ciphers, namely AES, Chaskey, Fantomas, HIGHT, LBlock, LEA, LED, Piccolo, PRESENT, PRIDE, PRINCE, RC5, RECTANGLE, RoadRunneR, Robin, Simon, SPARX, Speck, and TWINE, on three microcontroller platforms: 8-bit AVR, 16-bit MSP430, and 32-bit ARM. Our results bring some new insights into the question of how well these lightweight ciphers are suited to secure the Internet of things. The benchmarking framework provides cipher designers with an easy-to-use tool to compare new algorithms with the state of the art and allows standardization organizations to conduct a fair and consistent evaluation of a large number of candidates
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