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Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters.

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We propose Cluster Pruning (CUP) for compressing and accelerating deep neural networks. Our approach prunes similar filters by clustering them based on features derived from both the incoming and outgoing weight connections. With CUP, we overcome two limitations of prior work-(1) nonuniform pruning: CUP can efficiently determine the ideal number of filters to prune in each layer of a neural network. This is in contrast to prior methods that either prune all layers uniformly or otherwise use resource-intensive methods such as manual sensitivity analysis or reinforcement learning to determine the ideal number. (2) Single-shot operation:We extend CUP to CUP-SS (for CUP single shot) whereby pruning is integrated into the initial training phase itself. This leads to large savings in training time compared to traditional pruning pipelines. Through extensive evaluation on multiple datasets (MNIST, CIFAR-10, and Imagenet) and models (VGG-16, Resnets-18/34/56) we show that CUP outperforms recent state of the art. Specifically, CUP-SS achieves 2.2× flops reduction for a Resnet-50 model trained on Imagenet while staying within 0.9% top-5 accuracy. It saves over 14 hours in training time with respect to the original Resnet-50. Code to reproduce results is available here 1 .