Biomedical Signal and Image Processing

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Stroke is one of the leading causes of death and disability. Clinically, to establish stroke patient prognosis, an accurate delineation of brain lesion is essential, which is time consuming and prone to subjective errors. In this paper, we propose a novel method call Deep Lesion Symmetry ConvNet to automatically segment chronic stroke lesions using MRI. An 8- layer 3D convolutional neural network is constructed to handle the MRI voxels. An additional CNN stream using the corresponding symmetric MRI voxels is combined, leading to a significant improvement in system performance.

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2D& 3D Segmentation of Nuclei From Developing Fly Eyes

A major goal of modern medicine is to develop stem cell based therapies for replacing or healing diseased and damaged tissues. When a stem cell turns into skin, heart, nerve or other cell type during normal development, it follows hard-wired genetic instruction manuals that dictate the exact patterns and levels at which different genes are turned on and off.

Exploring the interface between signal processing and life sciences. Efforts in DNA-based Digital Signal Processing
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