Beyond Labels: Visual Representations for Bone Marrow Cell Morphology
Recognition
- URL: http://arxiv.org/abs/2205.09880v1
- Date: Thu, 19 May 2022 22:05:46 GMT
- Title: Beyond Labels: Visual Representations for Bone Marrow Cell Morphology
Recognition
- Authors: Shayan Fazeli, Alireza Samiei, Thomas D. Lee, Majid Sarrafzadeh
- Abstract summary: We improve on the state-of-the-art methodologies of bone marrow cell recognition by deviating from sole reliance on labeled data.
Our experiments demonstrate significant performance improvements in conducting different bone marrow cell recognition tasks.
- Score: 3.4352862428120123
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Analyzing and inspecting bone marrow cell cytomorphology is a critical but
highly complex and time-consuming component of hematopathology diagnosis.
Recent advancements in artificial intelligence have paved the way for the
application of deep learning algorithms to complex medical tasks. Nevertheless,
there are many challenges in applying effective learning algorithms to medical
image analysis, such as the lack of sufficient and reliably annotated training
datasets and the highly class-imbalanced nature of most medical data. Here, we
improve on the state-of-the-art methodologies of bone marrow cell recognition
by deviating from sole reliance on labeled data and leveraging self-supervision
in training our learning models. We investigate our approach's effectiveness in
identifying bone marrow cell types. Our experiments demonstrate significant
performance improvements in conducting different bone marrow cell recognition
tasks compared to the current state-of-the-art methodologies.
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