A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study evaluates adversarial attacks and defenses for chest X-ray disease classification.
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled.…
Study benchmarks uncertainty quantification in chest X-ray classification.
Efficient deep learning model classifies fractures from X-rays.
DeTraC CNN detects COVID-19 from chest X-rays with high accuracy.
New method uses contours of segmented images for X-ray classification.
Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual ener…
Convolutional neural networks (CNNs) have been successfully employed in recent years for the detection of radiological abnormalities in medical images such as plain x-rays. To date, most studies use CNNs on individual examinations in isolation and discard previously available clinical information. In this study we set …
In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact object is a black hole or a neutron star. To assist this classification, we develop a Bayesian statistical model that makes use of the fact that …
Study identifies COVID-19 pneumonia from chest X-rays.
CoroNet detects COVID-19 from chest X-rays with high accuracy.
Current Flash X-ray single-particle diffraction Imaging (FXI) experiments, which operate on modern X-ray Free Electron Lasers (XFELs), can record millions of interpretable diffraction patterns from individual biomolecules per day. Due to the stochastic nature of the XFELs, those patterns will to a varying degree includ…
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images. The system focuses on studying the model behaviors related to multiple structural attributes. It allows users to …
Study removes bias from chest X-ray embeddings using orthogonalization.
Localization of chest pathologies in chest X-ray images is a challenging task because of their varying sizes and appearances. We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning. Our method leverages intermediate feature maps from CNN layers at di…
Regularity results for geodesic X-ray transform on nonsmooth manifolds
Local X-ray transform works well near boundaries in hyperbolic spaces.
Paper shows invertibility of tensor X-ray transform on certain manifolds.
Kernel-based learning predicts ICU escalation from COVID-19 chest X-rays.
Study shows stability in X-ray transform on specific hyperbolic manifolds.
SCRIB assigns multiple labels to each example to control class-specific prediction risks.
Data labeling is currently a time-consuming task that often requires expert knowledge. In research settings, the availability of correctly labeled data is crucial to ensure that model predictions are accurate and useful. We propose relatively simple machine learning-based models that achieve high performance metrics in…
Injective X-ray transform on Heisenberg group for regular functions.
Paper proves injectivity of non-abelian X-ray transform on certain spaces.
X-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to char…
Deep learning ensembles improve COVID-19 detection from chest X-rays.
On simple geodesic disks of constant curvature, we derive new functional relations for the geodesic X-ray transform, involving a certain class of elliptic differential operators whose ellipticity degenerates normally at the boundary. We then use these relations to derive sharp mapping properties for the X-ray transform…
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis i…
X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based…
New method eliminates domain size restrictions for X-ray transform inversion.
X-ray transform on H-type groups solved, revealing function injectivity.
Study proper sampling for X-ray transforms on simple surfaces.
Paper solves injectivity of X-ray transform on surfaces.
New proof of injectivity for broken non-abelian X-ray transform in Minkowski space.
Enhanced X-ray polarimetry with deep learning for better exposure times.
These are lecture notes for the course "Analysis and X-ray tomography". The course is a broad overview of various tools in analysis that can be used to study X-ray tomography. The focus is on tools and ideas, not so much on technical details and minimal assumptions. Only very basic functional analysis is assumed as bac…
Study X-ray transform on manifolds, desingularize, and improve mapping properties.
We show that the existence of a function in with constant geodesic X-ray transform imposes geometrical restrictions on the manifold. The boundary of the manifold has to be umbilical and in the case of a strictly convex Euclidean domain, it must be a ball. Functions with constant geodesic X-ray transform always …
Unique continuation for X-ray transforms of one-forms with partial data.
Guillarmou extends X-ray transform to magnetic and thermostat flows.
New insights into X-ray transform on hyperbolic disk, with functional relations and range characterizations.
Paper shows X-ray transform invertible on certain curved spaces.
Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limitation of these deep learning based denoisers is the requirement of high-quality noiseless ground truth images that are difficult to obtain in…
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images w…
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
Injectivity of geodesic X-ray transform on low-regularity manifolds.
In this paper, we present a deep-learning based method for estimating the 3D structure of a bone from a pair of 2D X-ray images. Our triplet loss-trained neural network selects the most closely matching 3D bone shape from a predefined set of shapes. Our predictions have an average root mean square (RMS) distance of 1.0…
We complete the microlocal study of the geodesic X-ray transform on Riemannian manifolds with Anosov geodesic flow initiated by Guillarmou and pursued by Guillarmou and the second author. We prove new stability estimates and clarify some properties of the operator , the generalized X-ray transform. These estimates…