Publications by authors named "Darpan Sanghavi"
Article Synopsis
- The study focuses on creating a deep learning digital pathology tool for accurately detecting, segmenting, and classifying nuclei in cancer tissues, addressing challenges in quantifying nuclear morphology in histologic images.
- This tool was trained on nucleus annotations to analyze H&E-stained slides from various cancer cohorts (BRCA, LUAD, PRAD), revealing significant differences in nuclear features like shape and size linked to genomic instability and cancer prognosis.
- Results highlighted that certain nuclear characteristics, particularly in fibroblasts, were associated with patient survival outcomes and gene expression related to tumor biology, paving the way for better understanding of cancer biomarkers.
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning.
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