59 research outputs found
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
No full textWelcome to the the CSAW-M dataset homepageThis page includes the files and metadata related to the CSAW-M, a curated dataset of mammograms with expert assessments of the masking of cancer.
CSAW-M is collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We trained deep learning models on CSAW-M to estimate the masking level, and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers — without being explicitly trained for these tasks — than its breast density counterparts.
Please find the paper corresponding to our work here and the GitHub repo here.CSAW-M Research Use LicensePlease read carefully all the terms and conditions of the CSAW-M Research Use License.
How to access the dataset:If you want to get access to the data, please use the "Request access to files" option above (currently, non-Swedish researchers need to have a general figshare account to be able to to request access). We will ask you to agree to our terms of conditions and provide us with some information about what you will use the data for. We will then receive the request and process it, after which you would be able to download all the files.If you use this Work, please cite our paper:@article{sorkhei2021csaw,
title={CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer},
author={Sorkhei, Moein and Liu, Yue and Azizpour, Hossein and Azavedo, Edward and Dembrower, Karin and Ntoula, Dimitra and Zouzos, Athanasios and Strand, Fredrik and Smith, Kevin},
year={2021}
}</div
Visualization of author citation network.
No full textEach point indicates one author. Green: Data Mining, magenta: Computer Vision and blue:Machine Learning.</p
Author Correction: High gradient terahertz-driven ultrafast photogun
No full textCorrection to: Nature Photonics https://doi.org/10.1038/s41566-024-01441-y, published online 14 May 2024.In the version of this article originally published, Timm Rohwer’s surname appeared incorrectly (Rowher) and has now been corrected in the HTML and PDF versions of the article.Author informationAuthor notes These authors contributed equally: Jianwei Ying, Xie He.Authors and Affiliations Key Laboratory for Laser Plasmas (Ministry of Education), Collaborative Innovation Center of IFSA, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China Jianwei Ying, Xie He, Dace Su, Lingbin Zheng, Jingui Ma, Peng Yuan & Dongfang Zhang Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron, Hamburg, Germany Tobias Kroh, Timm Rohwer, Moein Fakhari, Günther H. Kassier, Nicholas H. Matlis & Franz X. Kärtner Department of Physics and The Hamburg Centre for Ultrafast Imaging, Universität Hamburg, Hamburg, Germany Tobias Kroh & Franz X. KärtnerCorresponding authorsCorrespondence to Franz X. Kärtner or Dongfang Zhang
Synthetische Datengenerierung für die Kommissionierung mit Robotern
No full textAdvances in robotics, especially in computer vision, have led to the increasing use of robots in order picking. Deep Learning methods using CNN for computer vision purposes have shown good object detection and localization results. However, training neural networks requires a large amount of domain-specific labelled data. In this work, we generated synthetic data and converted it to the appropriate format to be fed to neural network. For this purpose, randomized camera angles, backgrounds, and object configuration are used for data augmentation. A generalized and balanced dataset is ensured by varying these parameters based on the properties of natural objects.Fortschritte in der Robotik, insbesondere in der Computer Vision, haben zu einem zunehmenden Einsatz von Robotern in der Kommissionierung geführt. Deep-Learning-Methoden, die CNN für Computer-Vision-Zwecke verwenden, haben gute Ergebnisse bei der Objekterkennung und -lokalisierung gezeigt. Das Trainieren neuronaler Netze erfordert jedoch eine große Menge an objektspezifisch markierten Daten. In diesem Beitrag haben wir synthetische Daten generiert und in ein geeignetes Format konvertiert, um damit neuronale netzte zu trainieren. Zu diesem Zweck werden randomisierte Kamerawinkel, Hintergründe und Objektkonfigurationen zur Datenerweiterung verwendet. Durch die Variation dieser Parameter auf der Grundlage der Eigenschaften natürlicher Objekte wird ein allgemeiner und ausgewogener Datensatz gewährleistet
Performance Improvement of True Time Delay Based Centralized Beamforming Control with the Modulation Instability Phenomenon for Wireless-Array Antennas
No full textThis paper proposes a novel method to enhance the performance of the
modulator for fifth-generation wireless communication (5G) by exploiting
modulation instability (MI). We show that MI can reduce the bias voltage of the
modulator () by generating carrier side-band gain, and increase the
modulation BandWidth (BW), resulting in higher channel capacity, without
changing the modulator structure. In receive mode of the array antenna, where
the signal is very weak, high-frequency amplification is a high demanding
solution to mitigate coverage issue.
We also present a developed microwave-photonic beamforming bit-controller
system for receiver-transmitter phased array antennas (PAAs), which are
essential for high-capacity wireless communications like 5G. We employ a
modulated frequency comb exploiting MI fiber to achieve an amplified true-time
delay (TTD) technique for wide-coverage PAA beamforming and show that it can
steer wideband high-frequency signal to a specific direction angle, avoiding
beam-squint.Comment: 7 pages, 10 figure
Using Telehealth for Diagnosis and Management of Non‐Alcoholic Fatty Liver Disease
No full textContent available: Author Interview and Author Audio Recordin
Performance Improvement of True Time Delay Based Centralized Beamforming Control with the Modulation Instability Phenomenon for Wireless-Array Antennas
No full textThis paper proposes a novel method to enhance the performance of the modulator for fifth-generation wireless communication (5G) by exploiting modulation instability (MI). We show that MI can reduce the bias voltage of the modulator () by generating carrier side-band gain, and increase the modulation BandWidth (BW), resulting in higher channel capacity, without changing the modulator structure. In receive mode of the array antenna, where the signal is very weak, high-frequency amplification is a high demanding solution to mitigate coverage issue. We also present a developed microwave-photonic beamforming bit-controller system for receiver-transmitter phased array antennas (PAAs), which are essential for high-capacity wireless communications like 5G. We employ a modulated frequency comb exploiting MI fiber to achieve an amplified true-time delay (TTD) technique for wide-coverage PAA beamforming and show that it can steer wideband high-frequency signal to a specific direction angle, avoiding beam-squint
Evaluation of Multi-Agent Path Finding Methods for their Application in Robotized Logistics Systems.
No full textSeit einigen Jahren existieren Logistiksysteme, in denen hunderte Autonome Mobile Roboter für die Kommissionierung oder Sortierung eingesetzt werden. Die Pfadplanung für die in diesen Systemen navigierenden Roboter ist ein aktuelles Problem, für dessen Lösung seit geraumer Zeit Multi-Agent Path Finding (MAPF) Verfahren verwendet werden. Bisher besteht jedoch kein Konsens darüber, welche Bewertungskriterien es erlauben, die Eignung einer MAPF-Methode für eine bestimmte Anwendung abzuschätzen. In dieser Arbeit leiten wir daher qualitative Bewertungskriterien her (Skalierbarkeit, Konfliktmanagement und Lösungsqualität), die eine solche Eignungsbeurteilung ermöglichen. Unter Verwendung dieser Kriterien stellen wir fest, dass für Robotic Mobile Fulfillment Systeme die Priority Based Search geeignet ist, während in robotisierten Sortiersystemen die Explicit Estimation Conflict Based Search zweckmäßig ist. Für roboterbasierte Produktionsversorgung wiederum, empfehlen wir die Conflict Based Search.In recent time, logistics systems have been in place in which hundreds of autonomous mobile robots are used for tasks such as picking or sorting. Path planning for the robots navigating in these systems is still a relevant problem, for which Multi-Agent Path Finding (MAPF) methods have been considered for some time. However, there has been no consensus on which evaluation criteria allow for estimating the suitability of a MAPF method for a particular application. In this work, we therefore derive qualitative evaluation criteria (scalability, conflict management, and solution quality), that enable such a suitability assessment. We use these criteria to find that Priority Based Search is suitable for Robotic Mobile Fulfillment systems, while Explicit Estimation Conflict Based Search is appropriate in robotic sorting systems. For robotic-based production logistics, we recommend the Conflict Based Search algorithm
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