AIMC Topic: Supervised Machine Learning

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Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation.

IEEE transactions on medical imaging
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common informat...

A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.

PloS one
The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this re...

Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural ne...

End-to-end novel visual categories learning via auxiliary self-supervision.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data,...

Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification.

Medical image analysis
Convolutional neural networks have achieved prominent success on a variety of medical imaging tasks when a large amount of labeled training data is available. However, the acquisition of expert annotations for medical data is usually expensive and ti...

Review of machine learning methods in soft robotics.

PloS one
Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the s...

A pre-training and self-training approach for biomedical named entity recognition.

PloS one
Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training dat...

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.

Medical image analysis
The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, ches...