AIMC Journal:
IEEE transactions on medical imaging

Showing 321 to 330 of 687 articles

Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations.

IEEE transactions on medical imaging Sep 30, 2021
While high-resolution pathology images lend themselves well to 'data hungry' deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this article, we propose a self-supervised convolutional neu...
Neural Networks, Computer Algorithms Supervised Machine Learning
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Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training.

IEEE transactions on medical imaging Sep 30, 2021
Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly ...
Machine Learning Phenotype Radiography Semantics X-Rays
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Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images.

IEEE transactions on medical imaging Sep 30, 2021
Data annotation is a fundamental precursor for establishing large training sets to effectively apply deep learning methods to medical image analysis. For cell segmentation, obtaining high quality annotations is an expensive process that usually requi...
Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Retina
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Few-Shot Learning by a Cascaded Framework With Shape-Constrained Pseudo Label Assessment for Whole Heart Segmentation.

IEEE transactions on medical imaging Sep 30, 2021
Automatic and accurate 3D cardiac image segmentation plays a crucial role in cardiac disease diagnosis and treatment. Even though CNN based techniques have achieved great success in medical image segmentation, the expensive annotation, large memory c...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Heart Supervised Machine Learning
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Generating Synthetic Labeled Data From Existing Anatomical Models: An Example With Echocardiography Segmentation.

IEEE transactions on medical imaging Sep 30, 2021
Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than...
Humans Neural Networks, Computer Reproducibility of Results Echocardiography Ultrasonography Image Processing, Computer-Assisted
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Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction.

IEEE transactions on medical imaging Sep 30, 2021
Early breast cancer screening through mammography produces every year millions of images worldwide. Despite the volume of the data generated, these images are not systematically associated with standardized labels. Current protocols encourage giving ...
Humans Neural Networks, Computer Female Breast Neoplasms Early Detection of Cancer Mammography Breast
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Weakly Supervised Cell Segmentation by Point Annotation.

IEEE transactions on medical imaging Sep 30, 2021
We propose weakly supervised training schemes to train end-to-end cell segmentation networks that only require a single point annotation per cell as the training label and generate a high-quality segmentation mask close to those fully supervised meth...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Benchmarking Supervised Machine Learning
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A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data.

IEEE transactions on medical imaging Sep 30, 2021
Medical image segmentation has achieved remarkable advancements using deep neural networks (DNNs). However, DNNs often need big amounts of data and annotations for training, both of which can be difficult and costly to obtain. In this work, we propos...
Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Contour Transformer Network for One-Shot Segmentation of Anatomical Structures.

IEEE transactions on medical imaging Sep 30, 2021
Accurate segmentation of anatomical structures is vital for medical image analysis. The state-of-the-art accuracy is typically achieved by supervised learning methods, where gathering the requisite expert-labeled image annotations in a scalable manne...
Humans Neural Networks, Computer Image Processing, Computer-Assisted
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Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images.

IEEE transactions on medical imaging Sep 30, 2021
Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) u...
Neural Networks, Computer Image Processing, Computer-Assisted Supervised Machine Learning Microscopy
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