Most image segmentation algorithms are trained on binary masks formulated as a
classification task per pixel. However, in applications such as medical imaging, this “black …

Skin lesion segmentation from dermoscopic images plays a vital role in early diagnoses and
prognoses of various skin diseases. However, it is a challenging task due to the large …

Glioblastomata are the most generally perceived fundamental brain malignant tumors
known as Gliomas, with different shape, size & sub regions. It is hard to segment all three …

Simple Summary Early diagnosis and treatment of cancer is crucial for the survival of cancer
patients. Pathologists can use computational pathology techniques to make the diagnosis …

In this study, a new method of extraction of pore networks from tomography images of
geological porous material has been introduced. Superpixels, a classic method of image …

Abstract Develo** a Universal Lesion Detector (ULD) that can detect various types of
lesions from the whole body is of great importance for early diagnosis and timely treatment …

Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is of great
significance for radiotherapy planning. However, it is a challenging task to learn powerful …

In the context of automatic medical image segmentation based on statistical learning, raters'
variability of ground truth segmentations in training datasets is a widely recognized issue …

In medical image analysis, collecting multiple annotations from different clinical raters is a
typical practice to mitigate possible diagnostic errors. For such multirater labels' learning …

We study two of the most popular performance metrics in medical image segmentation,
Accuracy and Dice, when the target labels are noisy. For both metrics, several statements …