ConferenceEacl 2026 19th Conference of the European Chapter of the Association for Computational Linguistics Proceedings of the Conference Vol 1 Long Papers · January 1, 2026
We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy–utility trade-off. STAMP selectively allocates privacy budgets across tokens by jointly considering (i) ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2025
Natural language processing (NLP) methods can annotate free-text radiology reports to create large datasets at the scale of an entire health system or beyond. Generalizing the disease classification across multiple organ systems inherently requires a compl ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2022
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD). For CT specifically, interpreting the performance of CAD algorithms can ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2020
Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep learning s ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2020
Lack of annotated data is a major challenge to machine learning algorithms, particularly in the field of radiology. Algorithms that can efficiently extract labels in a fast and precise manner are in high demand. Weak supervision is a compromise solution, p ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2019
Many researchers in the field of machine learning have addressed the problem of detecting anomalies within Computed Tomography (CT) scans. Training these machine learning algorithms requires a dataset of CT scans with identified anomalies (labels), usually ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2019
Our goal is to develop a 2.5D CNN model to detect multiple diseases in multiple organs in CT scans. In this study we investigated detection of 4 common diseases in the lungs, which are atelectasis, edema, pneumonia and nodule. Most existing algorithms for ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2019
Purpose: When conducting machine learning algorithms on classification and detection of abnormalities for medical imaging, many researchers are faced with the problem that it is hard to get enough labeled data. This is especially difficult for modalities s ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2019
Our goal is to investigate using only case-level labels extracted automatically from radiology reports to construct a multi-disease classifier for CT scans with deep learning method. We chose four lung diseases as a start: atelectasis, pulmonary edema, nod ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2019
Purpose To accurately segment organs from 3D CT image volumes using a 2D, multi-channel SegNet model consisting of a deep Convolutional Neural Network (CNN) encoder-decoder architecture. Method We trained a SegNet model on the extended cardiac-Torso (XCAT) ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2017
The purpose of this study was to develop metrics of local anatomical complexity and compare them with detectability of lung nodules in CT. Data were drawn retrospectively from a published perception experiment in which detectability was assessed in cases e ...
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ConferenceProgress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2017
The purpose of this study was to quantify the accuracy of coronary computed tomography angiography (CTA) stenosis measurements using newly developed physical coronary plaque models attached to a base dynamic cardiac phantom (Shelley Medical DHP-01). Corona ...
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Conference2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 · January 1, 2006Link to itemCite
ConferenceProceedings of SPIE the International Society for Optical Engineering · May 1, 1994
Spiral computed tomography (CT) is a new technique for rapidly acquiring volumetric data within the body. By combining a continuous gantry rotation and table feed, it is possible to image the entire thorax within a single breath-hold. This eliminates the v ...
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