Digital Pathology Solution Accelerator

https://www.databricks.com/solutions/accelerators/digital-pathology

The tumor proliferation speed or tumor growth is an important biomarker for predicting patient outcomes. Proper assessment of this biomarker is crucial for informing the decisions for the treatment plan for the patient. In a clinical setting, the most common method is to count mitotic figures under a microscope by a pathologist. The manual counting and subjectivity of the process pose a reproducibility challenge. This has been the main motivation for many efforts to automate this process and use advanced ML techniques.

One of the main challenges however for automating this task, is the fact that whole slide images are rather large. WSI images can vary anywhere between 0.5 to 3.5GB in size, and that can slow down the image preprocessing step which is necessary for any downstream ML application.

In this solution accelerator, we walk you through a step-by-step process to use databricks capabilities to perform image segmentation and pre-processing on WSI and train a binary classifier that produces a metastasis probability map over a whole slide image (WSI).

Click on the "Get instant access" button in the top right corner to clone the solution accelerator repo into your workspace. Once the repo is cloned into your workspace, please execute the **RUNME** notebook in the repo in order to create the cluster and job you can use to run the notebooks.

搜集汇总

背景与挑战

背景概述

该数字病理学解决方案加速器旨在应对肿瘤增殖速度评估中手动计数的主观性和可重复性挑战，推动自动化机器学习应用。它解决了全切片图像尺寸大可能导致的预处理延迟问题，并通过逐步流程指导用户利用Databricks进行图像分割、预处理，并训练二元分类器以生成全切片图像的转移概率图。