Pathology image analysis is an essential procedure for clinical diagnosis of numerous
diseases. To boost the accuracy and objectivity of the diagnosis, nowadays, an increasing …

The medical image analysis field has traditionally been focused on the development of
organ-, and disease-specific methods. Recently, the interest in the development of more …

Curating a large scale fully-annotated dataset can be both labour-intensive and expertise-
demanding, especially for medical images. To alleviate this problem, we propose to utilize …

Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to
diagnose heart disease. However, repetitive manual segmentation of these images requires …

Human disease identification from the scanned body parts helps medical practitioners make
the right decision in lesser time. Image segmentation plays a vital role in automated …

Multi-atlas segmentation has been widely applied to the analysis of brain MR images.
However, the state-of-the-art techniques in multi-atlas segmentation, including both patch …

The availability of training data for supervision is a frequently encountered bottleneck of
medical image analysis methods. While typically established by a clinical expert rater, the …

Contrastive learning has become a popular solution for few-shot Name Entity Recognization
(NER). The conventional configuration strives to reduce the distance between tokens with …

Diamond grinding wheels (DGWs) have a central role in cutting-edge industries such as
aeronautics or defense and spatial applications. Characterizations of DGWs are essential to …

To efficiently establish training databases for machine learning methods, collaborative and
crowdsourcing platforms have been investigated to collectively tackle the annotation effort …