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CODA (part 3): deep learning tissue structures labeling | HuBMAP | JHU-TMC v2

Year: 2024

Type: preprint

Abstract: In this section, we describe steps to create a basic semantic segmentation algorithm using CODA. CODA uses a modified resnet50 network adapted for semantic segmentation using an implementation of DeepLab. Here, we describe how to generate training datasets of manual annotations for a set of images, how to format the data for deep learning, and how to train and apply a CODA deep learning model.

DOI: 10.17504/protocols.io.81wgbz1x3gpk/v2

Authors: Kyu Sang Han, Pei-Hsun Wu, Joel Sunshine, Ashley Kiemen, Sashank Reddy, Denis Wirtz

Institutions: Johns Hopkins University, Johns Hopkins Medicine

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Funder: National Cancer Institute

Award: G601512365

OpenAlex ID: W4394907657

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