17 research outputs found
Galaxy shape measurement with convolutional neural networks
We present our results from training and evaluating a convolutional neural
network (CNN) to predict galaxy shapes from wide-field survey images of the
first data release of the Dark Energy Survey (DES DR1). We use conventional
shape measurements as ground truth from an overlapping, deeper survey with less
sky coverage, the Canada-France Hawaii Telescope Lensing Survey (CFHTLenS). We
demonstrate that CNN predictions from single band DES images reproduce the
results of CFHTLenS at bright magnitudes and show higher correlation with
CFHTLenS at fainter magnitudes than maximum likelihood model fitting estimates
in the DES Y1 im3shape catalogue. Prediction of shape parameters with a CNN is
also extremely fast, it takes only 0.2 milliseconds per galaxy, improving more
than 4 orders of magnitudes over forward model fitting. The CNN can also
accurately predict shapes when using multiple images of the same galaxy, even
in different color bands, with no additional computational overhead. The CNN is
again more precise for faint objects, and the advantage of the CNN is more
pronounced for blue galaxies than red ones when compared to the DES Y1
metacalibration catalogue, which fits a single Gaussian profile using riz band
images. We demonstrate that CNN shape predictions within the metacalibration
self-calibrating framework yield shear estimates with negligible multiplicative
bias, , and no significant PSF leakage. Our proposed setup is
applicable to current and next generation weak lensing surveys where higher
quality ground truth shapes can be measured in dedicated deep fields
An improved cosmological parameter inference scheme motivated by deep learning
Dark matter cannot be observed directly, but its weak gravitational lensing
slightly distorts the apparent shapes of background galaxies, making weak
lensing one of the most promising probes of cosmology. Several observational
studies have measured the effect, and there are currently running, and planned
efforts to provide even larger, and higher resolution weak lensing maps. Due to
nonlinearities on small scales, the traditional analysis with two-point
statistics does not fully capture all the underlying information. Multiple
inference methods were proposed to extract more details based on higher order
statistics, peak statistics, Minkowski functionals and recently convolutional
neural networks (CNN). Here we present an improved convolutional neural network
that gives significantly better estimates of and
cosmological parameters from simulated convergence maps than the state of art
methods and also is free of systematic bias. We show that the network exploits
information in the gradients around peaks, and with this insight, we construct
a new, easy-to-understand, and robust peak counting algorithm based on the
'steepness' of peaks, instead of their heights. The proposed scheme is even
more accurate than the neural network on high-resolution noiseless maps. With
shape noise and lower resolution its relative advantage deteriorates, but it
remains more accurate than peak counting
Detecting and classifying lesions in mammograms with Deep Learning
In the last two decades Computer Aided Diagnostics (CAD) systems were
developed to help radiologists analyze screening mammograms. The benefits of
current CAD technologies appear to be contradictory and they should be improved
to be ultimately considered useful. Since 2012 deep convolutional neural
networks (CNN) have been a tremendous success in image recognition, reaching
human performance. These methods have greatly surpassed the traditional
approaches, which are similar to currently used CAD solutions. Deep CNN-s have
the potential to revolutionize medical image analysis. We propose a CAD system
based on one of the most successful object detection frameworks, Faster R-CNN.
The system detects and classifies malignant or benign lesions on a mammogram
without any human intervention. The proposed method sets the state of the art
classification performance on the public INbreast database, AUC = 0.95 . The
approach described here has achieved the 2nd place in the Digital Mammography
DREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches
high sensitivity with very few false positive marks per image on the INbreast
dataset. Source code, the trained model and an OsiriX plugin are availaible
online at https://github.com/riblidezso/frcnn_cad
Weak lensing cosmology with convolutional neural networks on noisy data
Weak gravitational lensing is one of the most promising cosmological probes
of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST,
EUCLID, WFIRST) astronomical surveys attempt to collect even deeper and larger
scale data on weak lensing. Due to gravitational collapse, the distribution of
dark matter is non-Gaussian on small scales. However, observations are
typically evaluated through the two-point correlation function of galaxy shear,
which does not capture non-Gaussian features of the lensing maps. Previous
studies attempted to extract non-Gaussian information from weak lensing
observations through several higher-order statistics such as the three-point
correlation function, peak counts or Minkowski-functionals. Deep convolutional
neural networks (CNN) emerged in the field of computer vision with tremendous
success, and they offer a new and very promising framework to extract
information from 2 or 3-dimensional astronomical data sets, confirmed by recent
studies on weak lensing. We show that a CNN is able to yield significantly
stricter constraints of () cosmological parameters than the
power spectrum using convergence maps generated by full N-body simulations and
ray-tracing, at angular scales and shape noise levels relevant for future
observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4-2.8
times smaller credible contours than the power spectrum, and 3.5-4.2 times
smaller at noise levels corresponding to a deep space survey such as WFIRST. We
also show that at shape noise levels achievable in future space surveys the CNN
yields 1.4-2.1 times smaller contours than peak counts, a higher-order
statistic capable of extracting non-Gaussian information from weak lensing
maps
An improved cosmological parameter inference scheme motivated by deep learning
Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes of cosmology. Several observational studies have measured the effect, and there are currently running(1,2) and planned efforts(3,4) to provide even larger and higher-resolution weak lensing maps. Owing to nonlinearities on small scales, the traditional analysis with two-point statistics does not fully capture all of the underlying informations(5). Multiple inference methods have been proposed to extract more details based on higher-order statistics(6,7), peak statisticss(8-13), Minkowski functionals(14-16) and recently convolutional neural networks(17,18). Here we present an improved convolutional neural network that gives significantly better estimates of the Omega(m) and sigma(8) cosmological parameters from simulated weak lensing convergence maps than state-of-art methods and that is also free of systematic bias. We show that the network exploits information in the gradients around peaks, and with this insight we have constructed an easy-to-understand and robust peak-counting algorithm based on the steepness of peaks, instead of their heights. The proposed scheme is even more accurate than the neural network on high-resolution noiseless maps. With shape noise and lower resolution, its relative advantage deteriorates, but it remains more accurate than peak counting
A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában = Potential applications of deep learning-based technologies in Hungarian mammography
Absztrakt:
Bevezetés és célkitűzés: A számítógépes ’mélytanulás’ (deep
learning) az elmúlt két évtized számítástechnikai fejlődésének legjelentősebb
ajándéka. A számítógépes mélytanulásban rejlő – egyelőre még beláthatatlan –
lehetőségek megértése, befogadása és alkalmazása a medicina megkerülhetetlen
feladata. Módszer: Ajándék és feladat, hiszen az
exponenciálisan növekvő adatok (képalkotó vizsgálati, laboratóriumi,
terápiaválasztási lehetőségek, terápia-kölcsönhatások stb.) „bitjeinek”
tengerében minden vágyunk és deklarációnk ellenére mind kevésbé tudjuk a
személyre és állapotra, a tumorra és környezetére szabott
individuális ellátást megvalósítani. Eredmények: A
jelen pillanatban felelős ellátóként – és nem kevésbé felelős finanszírozóként –
azt élhetjük meg, hogy egyéni és közösségi szinten is szuboptimális folyamatokat
tartunk fenn, aminek oka egyszerre az adatok bősége, ugyanakkor az ellátáshoz
individuálisan fontos adatok hiánya. A számítógépes mélytanulás, a medicina
lényegét adó ember–ember közti találkozás gyógyító erejét nem csorbítva – hanem
inkább kiterjesztve –, ebben kínál fényt az alagútban.
Következtetés: Belátva tehát saját adatintegrációs és
ismereti korlátainkat, nekünk, orvosoknak és ellátásfinanszírozóknak – sajátos
előítéleteinket és félelmeinket feladva – kell megtanulni a számítógépes
mélytanulásban rejlő különleges lehetőségeket, melyek nemcsak a képalkotó
diagnosztikában, hanem már napi realitásként a terápia területén is használhatók
(immunterápia). A közlemény ehhez igyekszik kedvet csinálni. Orv Hetil. 2019;
160(4): 138–143.
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Abstract:
Introduction and aim: The technology, named ‘deep learning’ is
the promising result of the last two decades of development in computer science.
It poses an unavoidable challenge for medicine, how to understand, apply and
adopt the – today not fully explored – possibilities that have become available
by these new methods. Method: It is a gift and a mission, since
the exponentially growing volume of raw data (from imaging, laboratory, therapy
diagnostics or therapy interactions, etc.) did not solve until now our wished
and aimed goal to treat patients according to their personal status and setting
or specific to their tumor and disease. Results: Currently, as
a responsible health care provider and financier, we face the problem of
supporting suboptimal procedures and protocols either at individual or at
community level. The problem roots in the overwhelming amount of data and, at
the same time, the lack of targeted information for treatment. We expect from
the deep learning technology an aid which helps to reinforce and extend the
human–human cooperations in patient–doctor visits. We expect that computers take
over the tedious work allowing to revive the core of healing medicine: the
insightful meeting and discussion between patients and medical experts.
Conclusion: We should learn the revelational possibilities
of deep learning techniques that can help to overcome our recognized finite
capacities in data processing and integration. If we, doctors and health care
providers or decision makers, are able to abandon our fears and prejudices, then
we can utilize this new tool not only in imaging diagnostics but also for daily
therapies (e.g., immune therapy). The paper aims to make a
great mind to do this. Orv Hetil. 2019; 160(4): 138–143