PV21 PRESENTER

 

Ho_David

David J. Ho, PhD

Machine Learning Scientist

Memorial Sloan Kettering Cancer Center

 

 

Presenting

Deep learning-based whole slide image segmentation for efficient and reproducible assistance in pathology

 

Abstract

Background: Pathology plays a crucial role to diagnose cancer and to assess its progression from H&E-stained tissue samples. Diagnosis and assessment have been done under microscopes which can be inefficient and subjective. Digitization of glass slides and deep learning-based computational approaches have been investigated to help this process. Especially, semantic segmentation, also known as pixel-wise classification, of whole slide images providing information of location and size of multiple tissue subtypes is a prerequisite step for clinical interpretations. We introduce how tissue segmentation by Deep Multi-Magnification Network (DMMN) can assist cancer diagnosis and assessment in an efficient and reproducible manner.

 

Methods: DMMN looks at morphological features from multiple magnifications for more accurate segmentation. We trained three DMMN models to help pathologists in clinical settings: (1) a breast model segmenting cancer to screen malignant margin slides where most of margins are generally benign, (2) an osteosarcoma model segmenting viable tumor and necrotic tumor to calculate case-level necrosis ratio from multiple slides for pre-operative treatment response assessment, (3) a lung model segmenting multiple tumor subtypes to find the predominant pattern.

 

Results: The breast model selected malignant margin slides and highlighted cancer regions with high sensitivity. The osteosarcoma model estimated case-level necrosis ratio with an acceptable error rate comparing to pathologists' manual assessment. The lung model determined the predominant pattern based on multi-class segmentation.

 

Conclusion: Segmentation models we developed can provide efficient and objective supports to pathologists. We plan to apply these models to clinical settings to reduce pathologists' assessment time and error.

 

Objectives

  1. Understand how tissue segmentation of whole slide images is done by deep learning
  2. Understand how segmentation can assist diagnosis and assessment of various cancer types

 

Biography

Dr. David Joon Ho is a machine learning scientist at Memorial Sloan Kettering Cancer Center. He received his BS and MS degrees from the University of Illinois at Urbana-Champaign and PhD degree from Purdue University, all in electrical and computer engineering. His research interests include digital and computational pathology, computer vision, and machine learning/deep learning. More specifically, He works on multi-class tissue segmentation of histopathology whole slide images from various cancer types and pursue further analyses such as treatment response assessment, mutation prediction, and treatment response prediction.


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