3,700 research outputs found
Enhanced Breast Cancer Classification with Automatic Thresholding Using Support Vector Machine and Harris Corner Detection
Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as benign and malignant classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is timeconsuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, having a better training data set directly affect the quality of classification process. In this paper, a method is proposed based on supervised learning and automatic thresholding for both generating better training data set, and more accurate classification of the mammogram images into benign/malignant classes. The procedure consists of pre-processing, removing noise, elimination of unwanted objects, features extraction, and classification. A Support Vector Machine (SVM) is used as the supervised model in two phases which are testing and training. Intensity value, auto-correlation matrix value of detected corners, and, energy, are three extracted features used to train the SVM. Experimental results show this method classify images with more accuracy and less execution time compared to the existing method
Recent Progress in Transformer-based Medical Image Analysis
The transformer is primarily used in the field of natural language
processing. Recently, it has been adopted and shows promise in the computer
vision (CV) field. Medical image analysis (MIA), as a critical branch of CV,
also greatly benefits from this state-of-the-art technique. In this review, we
first recap the core component of the transformer, the attention mechanism, and
the detailed structures of the transformer. After that, we depict the recent
progress of the transformer in the field of MIA. We organize the applications
in a sequence of different tasks, including classification, segmentation,
captioning, registration, detection, enhancement, localization, and synthesis.
The mainstream classification and segmentation tasks are further divided into
eleven medical image modalities. A large number of experiments studied in this
review illustrate that the transformer-based method outperforms existing
methods through comparisons with multiple evaluation metrics. Finally, we
discuss the open challenges and future opportunities in this field. This
task-modality review with the latest contents, detailed information, and
comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte
Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field
that aims to design computer agents with intelligent capabilities such as
understanding, reasoning, and learning through integrating multiple
communicative modalities, including linguistic, acoustic, visual, tactile, and
physiological messages. With the recent interest in video understanding,
embodied autonomous agents, text-to-image generation, and multisensor fusion in
application domains such as healthcare and robotics, multimodal machine
learning has brought unique computational and theoretical challenges to the
machine learning community given the heterogeneity of data sources and the
interconnections often found between modalities. However, the breadth of
progress in multimodal research has made it difficult to identify the common
themes and open questions in the field. By synthesizing a broad range of
application domains and theoretical frameworks from both historical and recent
perspectives, this paper is designed to provide an overview of the
computational and theoretical foundations of multimodal machine learning. We
start by defining two key principles of modality heterogeneity and
interconnections that have driven subsequent innovations, and propose a
taxonomy of 6 core technical challenges: representation, alignment, reasoning,
generation, transference, and quantification covering historical and recent
trends. Recent technical achievements will be presented through the lens of
this taxonomy, allowing researchers to understand the similarities and
differences across new approaches. We end by motivating several open problems
for future research as identified by our taxonomy
Particularities of visualisation of medical and wellness data through a digital patient avatar
In this work particularities of visualisation of medical and wellness data through a digital patient avatar are given from a standpoint of a proposed approach, under which data for a visualisation may be obtained from a variety of sources through defined interfaces, while end-user interfaces of distinct complexity and level of immersion into the model may be exposed to different categories of users. A short introduction of important medical data exchange standards, specifications and models is offered. A brief overview of projects relevant to a subject of this work is given. The proposed approach is presented along with examples of use-cases
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
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