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Quality scores assigned to blastocyst inner cell mass (ICM), trophectoderm (TE), and zona pellucida (ZP) are critical markers for predicting implantation potential of a human blastocyst in IVF treatment. Deep Convolutional Neural Networks (CNNs) have shown success in various image classification tasks, including classification of blastocysts into two quality categories. However, the problem of multi-label multi-class classification for blastocyst grading remains unsolved.

A quantitative understanding of complex biological systems such as tissues requires reconstructing the structure of the different components of the system. Fluorescence microscopy provides the means to visualize simultaneously several tissue components. However, it can be time consuming and is limited by the number of fluorescent markers that can be used. In this study, we describe a toolbox of algorithms based on convolutional neural networks for the prediction of 3D tissue structures by learning features embedded within single-marker images.

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects.

Automated curvilinear image segmentation is a crucial step to characterize and quantify the morphology of blood vessels across scale. We propose a dual pipeline RF_OFB+U-NET that fuses U-Net deep learning features with a low level image feature filter bank using the random forests classifier for vessel segmentation. We modify the U-Net CNN architecture to provide a foreground vessel regression likelihood map that is used to segment both arteriole and venule blood vessels in mice dura mater tissues.

Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Automatic liver lesion classification on computed
tomography images is of great importance to early cancer
diagnosis and remains a challenging task. State-of-the-art
liver lesion classification algorithms are currently based on
manually selected regions of interest (ROIs) or automatically
detected ROIs. However, liver lesions usually vary in size
and shape, which makes the ROI selection process laborintensive
and also poses an obstacle to automatic lesion
detection. In this paper, we propose a dual-attention dilated

Conventional medical image registration relies on time-consuming iterative optimization. We propose a two-stage unsupervised learning method for 3D medical image registration. In the first stage, we learn a global image-wise affine map by a deep network. In the second stage, we learn a local voxel-wise deformation vector field by an encoder-decoder architecture. The final registered image is acquired by applying the local deformation field to the moved image of the first stage.