AIMC Topic: Diagnostic Imaging
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Deep computational pathology in breast cancer.
Seminars in cancer biology
Aug 17, 2020
Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular ...
Integration of artificial intelligence into clinical patient management: focus on cardiac imaging.
Revista espanola de cardiologia (English ed.)
Aug 17, 2020
Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, st...
Variability and reproducibility in deep learning for medical image segmentation.
Scientific reports
Aug 13, 2020
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep le...
Using imaging to combat a pandemic: rationale for developing the UK National COVID-19 Chest Imaging Database.
The European respiratory journal
Aug 13, 2020
https://bit.ly/3eQeuha
Handling imbalanced medical image data: A deep-learning-based one-class classification approach.
Artificial intelligence in medicine
Aug 7, 2020
In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying ...
Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jul 31, 2020
Lung cancer remains the most common cause of cancer death worldwide. Recent advances in lung cancer screening, radiotherapy, surgical techniques, and systemic therapy have led to increasing complexity in diagnosis, treatment decision-making, and asse...
Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jul 31, 2020
Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in ...
Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors.
Radiology. Imaging cancer
Jul 31, 2020
Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of prediction algorithms represent a new era for noninvasive biomarker discovery. In recent literature, it has b...
Causality matters in medical imaging.
Nature communications
Jul 22, 2020
Causal reasoning can shed new light on the major challenges in machine learning for medical imaging: scarcity of high-quality annotated data and mismatch between the development dataset and the target environment. A causal perspective on these issues...
An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.
Sensors (Basel, Switzerland)
Jul 13, 2020
The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper ...
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