High Precision Mammography Lesion Identification From Imprecise Medical Annotations

Frontiers in Big Data

Ankit Bhardwaj¹  Ulzee An²  Lakshminarayanan Subramanian³  Khader Shameer⁴ 

[1] Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United States;Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, United States;Department of Population Health, NYU Grossman School of Medicine, New York University, New York, NY, United States;Northwell Health, New York, NY, United States;

关键词: oncology;    breast cancer;    computer vision;    digital health;    computational diagnosis;    big data and analytics;   

DOI  :  10.3389/fdata.2021.742779

来源: DOAJ

【 摘 要 】

Breast cancer screening using Mammography serves as the earliest defense against breast cancer, revealing anomalous tissue years before it can be detected through physical screening. Despite the use of high resolution radiography, the presence of densely overlapping patterns challenges the consistency of human-driven diagnosis and drives interest in leveraging state-of-art localization ability of deep convolutional neural networks (DCNN). The growing availability of digitized clinical archives enables the training of deep segmentation models, but training using the most widely available form of coarse hand-drawn annotations works against learning the precise boundary of cancerous tissue in evaluation, while producing results that are more aligned with the annotations rather than the underlying lesions. The expense of collecting high quality pixel-level data in the field of medical science makes this even more difficult. To surmount this fundamental challenge, we propose LatentCADx, a deep learning segmentation model capable of precisely annotating cancer lesions underlying hand-drawn annotations, which we procedurally obtain using joint classification training and a strict segmentation penalty. We demonstrate the capability of LatentCADx on a publicly available dataset of 2,620 Mammogram case files, where LatentCADx obtains classification ROC of 0.97, AP of 0.87, and segmentation AP of 0.75 (IOU = 0.5), giving comparable or better performance than other models. Qualitative and precision evaluation of LatentCADx annotations on validation samples reveals that LatentCADx increases the specificity of segmentations beyond that of existing models trained on hand-drawn annotations, with pixel level specificity reaching a staggering value of 0.90. It also obtains sharp boundary around lesions unlike other methods, reducing the confused pixels in the output by more than 60%.

【 授权许可】

Unknown