Margin Assessment of Extramammary Paget's Disease Based On Harmonic Generation Microscopy With Deep Neural Networks

MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis

With the growing complexity of machine learning techniques, understanding the functioning of black-box models is more important than ever. A recently popular strategy towards interpretability is to generate explanations based on examples – called influential samples – that have the largest influence on the model’s observed behavior. However, for such an analysis, we are confronted with a pletho...

Large Margin Deep Neural Networks: Theory and Algorithms

Deep neural networks (DNN) have achieved huge practical success in recent years. However, its theoretical properties (in particular generalization ability) are not yet very clear, since existing error bounds for neural networks cannot be directly used to explain the statistical behaviors of practically adopted DNN models (which are multi-class in their nature and may contain convolutional layer...

Interferometric second harmonic generation microscopy.

We describe a novel second harmonic generation (SHG) microscope that employs heterodyne detection by interfering the epi directed SHG from a sample with SHG from a reference crystal. In addition, the microscope provides complementary reflectance information based on optical coherence microscopy (OCM). The instrument features dual balanced detection to minimize the effect of source fluctuations,...

Adaptive harmonic generation microscopy of mammalian embryos.

Adaptive optics is implemented in a harmonic generation microscope using a wavefront sensorless correction scheme. Both the second- and third-harmonic intensity signals are used as the optimization metric. Aberration correction is performed to compensate both system- and specimen-induced aberrations by using an efficient optimization routine based upon Zernike polynomial modes. Images of live m...

Pagets Disease Of The Nipple