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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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PySeqLab: an open source Python package for sequence labeling and segmentation.

Ahmed Allam¹, Michael Krauthammer^1,2

¹Department of Pathology, Yale School of Medicine, New Haven, CT 06511, USA.

Bioinformatics (Oxford, England)

|October 17, 2017

Summary

This summary is machine-generated.

This study introduces Python Sequence Labeling (PySeqLab), a library for conditional random fields (CRFs) to predict structures in sequential data like text and DNA. PySeqLab achieves state-of-the-art results across biomedical NLP, DNA analysis, and activity recognition without manual feature engineering.

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Area of Science:

Computational biology
Bioinformatics
Natural Language Processing

Background:

Sequential data, including text and genomic sequences, possess inherent structures crucial for analysis.
Conditional Random Fields (CRFs) are powerful probabilistic graphical models for predicting these structures.

Purpose of the Study:

To develop a comprehensive Python library, PySeqLab, for implementing and applying CRFs to sequential data.
To provide robust tools for supervised learning in structured prediction tasks.

Main Methods:

PySeqLab implements various first-order to higher-order linear-chain and semi-Markov CRFs.
It incorporates multiple parameter estimation algorithms including SGD, structured perceptron, SAPO, and BFGS variants.
Inference and decoding are performed using Viterbi and Viterbi A* algorithms.

Main Results:

PySeqLab was evaluated on biomedical NLP, DNA sequence analysis, and Human Activity Recognition (HAR) tasks.
Models built with PySeqLab achieved state-of-the-art performance, comparable to existing machine learning systems.
The library enables high performance without requiring manual feature engineering or external knowledge sources.

Conclusions:

PySeqLab offers a versatile and effective toolkit for structured prediction using CRFs.
The library demonstrates the capability to achieve top-tier results in diverse sequential data domains.