A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence of an on-premise reporting Radiologist, which is a challenge in low and middle income countries. This has inspired the development of machine learning based automation of the screening process. While recent efforts demonstrate a performance benchmark using an ensemble of deep convolutional neural networks (CNN), our systematic search over multiple standard CNN architectures identified single candidate CNN models whose classification performances were found to be at par with ensembles. Over 63 experiments spanning 400 hours, executed on a 11:3 FP32 TensorTFLOPS compute system, we found the Xception and ResNet-18 architectures to be consistent performers in identifying co-existing disease conditions with an average AUC of 0.87 across nine pathologies. We conclude on the reliability of the models by assessing their saliency maps generated using the randomized input sampling for explanation (RISE) method and qualitatively validating them against manual annotations locally sourced from an experienced Radiologist. We also draw a critical note on the limitations of the publicly available CheXpert dataset primarily on account of disparity in class distribution in training vs. testing sets, and unavailability of sufficient samples for few classes, which hampers quantitative reporting due to sample insufficiency.Comment: accepted in EMBC 2020, 4 pages+2 page Appendi

Disseminated protothecosis caused by Prototheca zopfii in a liver transplant recipient

Prototheca is a genus of achlorophyllic algae present ubiquitously in the environment. Human infections are rare affecting immunocompromised individuals. We report a case of fatal algaemia caused by Prototheca zopfii in a patient who underwent liver transplant. Tissue diagnosis is mandatory for diagnosing rare entities in seriously ill, immunocompromised individuals

MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication.

With the evolution of multicellularity, communication among cells in different tissues and organs became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide screens for genes and other biomolecules mediating tissue-tissue signaling are lacking. To systematically identify inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs. across-layer connectivity to quantify the "influence" of any gene in a tissue on a query set of genes of interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability, and performs well on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain regions in Alzheimer's disease. MultiCens-prioritized hypotheses from these two diverse applications, and potential future ones like "Multi-tissue-expanded Gene Ontology" analysis, can enable whole-body yet molecular-level systems investigations in humans

A technique for reducing complexity of recursive motion estimation algorithms

The recursive search motion estimation algorithm offers sm-ooth and accurate motion vector elds. Computationally, the most expensive part of the motion estimator is the evalu-ation of the various motion vector candidates. Evaluation is performed by comparing blocks in two consecutive frames pointed by motion vector candidates. This paper addresses the issue of reducing the already extremely low number of motion vector evaluations. We apply pre-processing tech-niques to reduce the number of motion vector candidates from 7 to 5, i.e. 30 % without sacricing quality. We ex-emplify the above ndings through experimental results ob-tained using the 3-D recursive search motion estimation al-gorithm. The required pre-processing overhead is negligi-ble. 1

A 27 mW 1.1 mm2 motion estimator for picture-rate up-converter

The gap between application-specific integrated circuits (ASICs) and general purpose programmable processors in terms of performance, power, cost and flexibility is well known. Application specific instruction set processors (ASIPs) bridge this wide gap. This work presents a design of a very long instruction word (VLIW) based ASIP for motion estimation which is used in the picture-rate up-conversion application. The ASIP meets low-power and low-cost requirements apart from providing flexibility for the application domain. It consumes 27 mW and takes an area of 1.1 mm2 in 0.13 μm technology for delivering motion estimation functionality for standard definition (SD) sequences at 140fps. Motion estimator performed single scan, where for each block of 8*8 pixels evaluation is done using the set of five motion vector candidates. The evaluation criterion was the sum-of-absolute-difference (SAD) criterion with the SAD window size of 32 pixels. In order to prove the concept in silicon, an FPGA prototyping system has been used