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MedMamba: Vision Mamba for Medical Image Classification
arXiv.orgSince the era of deep learning, convolutional neural networks (CNNs) and vision transformers (ViTs) have been extensively studied and widely used in medical image classification tasks.
Y. Yue, Zhenzhang Li
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Mutual learning with reliable pseudo label for semi-supervised medical image segmentation
Medical Image Anal.Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated ...
Jiawei Su +4 more
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Content-Based Medical Image Retrieval for Medical Radiology Images [PDF]
In this study, a CBIR system for medical radiology images of various anatomical regions and imaging modalities was designed and implemented. Three descriptor-extraction retrieval methods were evaluated: BoVW, HOG, and CNN. Optimal configuration for each method was selected empirically. We used a dataset consisting of 38, 000 2D images, originating from
Dario Barac +6 more
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A review of convolutional neural network based methods for medical image classification
Comput. Biol. MedicineThis study systematically reviews CNN-based medical image classification methods. We surveyed 149 of the latest and most important papers published to date and conducted an in-depth analysis of the methods used therein.
Chao Chen, N. A. Mat Isa, Xin Liu
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The Journal of the Acoustical Society of America, 2013
In a system for diagnoses of mammary glands and breasts using radiation and ultrasonic waves in combination, images are correlated for easier visual recognition. The system includes an apparatus for acquiring a radiation image of an object projected on a projection surface, acquiring an ultrasonic slice image of the object along a slice surface ...
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In a system for diagnoses of mammary glands and breasts using radiation and ultrasonic waves in combination, images are correlated for easier visual recognition. The system includes an apparatus for acquiring a radiation image of an object projected on a projection surface, acquiring an ultrasonic slice image of the object along a slice surface ...
openaire +1 more source
SPIE Proceedings, 2015
This paper presents progress on imaging the research field of Imaging Informatics, mapped as the clustering of its communities together with their main results by applying a process to produce a dynamical image of the interactions between their results and their common object(s) of research.
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This paper presents progress on imaging the research field of Imaging Informatics, mapped as the clustering of its communities together with their main results by applying a process to produce a dynamical image of the interactions between their results and their common object(s) of research.
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2016
Imaging is one of the most important sources of clinically observable evidence that provides broad coverage, can provide insight on low-level scale properties, is noninvasive, has few side effects, and can be performed frequently. Thus, imaging data provides a viable observable that can facilitate the instantiation of a theoretical understanding of a ...
William, Hsu +2 more
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Imaging is one of the most important sources of clinically observable evidence that provides broad coverage, can provide insight on low-level scale properties, is noninvasive, has few side effects, and can be performed frequently. Thus, imaging data provides a viable observable that can facilitate the instantiation of a theoretical understanding of a ...
William, Hsu +2 more
openaire +2 more sources
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models
International Conference on Medical Image Computing and Computer-Assisted InterventionDiffusion models have enabled remarkably high-quality medical image generation, yet it is challenging to enforce anatomical constraints in generated images.
N. Konz +3 more
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MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation
International Conference on Medical Image Computing and Computer-Assisted InterventionMedical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning. To date, great progress has been made in deep learning-based segmentation techniques, but most methods still
Taha Koleilat +3 more
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MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
International Conference on Medical Image Computing and Computer-Assisted InterventionIn the field of medical decision-making, precise anomaly detection in medical imaging plays a pivotal role in aiding clinicians. However, previous work is reliant on large-scale datasets for training anomaly detection models, which increases the ...
Ximiao Zhang +5 more
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