• Corpus ID: 238531419

Multiple Myeloma Cancer Cell Instance Segmentation

  title={Multiple Myeloma Cancer Cell Instance Segmentation},
  author={Dikshant Sagar},
Images remain the largest data source in the field of healthcare. But at the same time, they are the most difficult to analyze. More than often, these images are analyzed by human experts such as pathologists and physicians. But due to considerable variation in pathology and the potential fatigue of human experts, an automated solution is much needed. The recent advancement in Deep learning could help us achieve an efficient and economical solution for the same. In this research project, we… 


PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma
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.
Focal Loss for Dense Object Detection
This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
The proposed CSPNet respects the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset.
Feature Pyramid Networks for Object Detection
This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
A Survey of Deep Learning-Based Object Detection
This survey provides a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors, and lists the traditional and new applications.
Learning Multiple Layers of Features from Tiny Images
It is shown how to train a multi-layer generative model that learns to extract meaningful features which resemble those found in the human visual cortex, using a novel parallelization algorithm to distribute the work among multiple machines connected on a network.
Deep Residual Learning for Image Recognition
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Aggregated Residual Transformations for Deep Neural Networks
On the ImageNet-1K dataset, it is empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy and is more effective than going deeper or wider when the authors increase the capacity.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
The adopted Neural Architecture Search is adopted and a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections is discovered, named NAS-FPN, which achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models.