Dr. Benjamin C. Calhoun Profile

Dr. Benjamin C. Calhoun

at Cleveland Clinic

SPIE Involvement:

Author

Publications (2)

SPIE Journal Paper | 23 August 2024 Open Access

Stain SAN: simultaneous augmentation and normalization for histopathology images

Taebin Kim, Yao Li, Benjamin Calhoun, Aatish Thennavan, Lisa A. Carey, W. Fraser Symmans, Melissa Troester, Charles Perou, J.S. Marron

JMI, Vol. 11, Issue 04, 044006, (August 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.4.044006

KEYWORDS: Education and training, Matrices, Histopathology, Color, Binary data, Color normalization, RGB color model, Image classification, Batch normalization, Visualization

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PurposeWe address the need for effective stain domain adaptation methods in histopathology to enhance the performance of downstream computational tasks, particularly classification. Existing methods exhibit varying strengths and weaknesses, prompting the exploration of a different approach. The focus is on improving stain color consistency, expanding the stain domain scope, and minimizing the domain gap between image batches.ApproachWe introduce a new domain adaptation method, Stain simultaneous augmentation and normalization (SAN), designed to adjust the distribution of stain colors to align with a target distribution. Stain SAN combines the merits of established methods, such as stain normalization, stain augmentation, and stain mix-up, while mitigating their inherent limitations. Stain SAN adapts stain domains by resampling stain color matrices from a well-structured target distribution.ResultsExperimental evaluations of cross-dataset clinical estrogen receptor status classification demonstrate the efficacy of Stain SAN and its superior performance compared with existing stain adaptation methods. In one case, the area under the curve (AUC) increased by 11.4%. Overall, our results clearly show the improvements made over the history of the development of these methods culminating with substantial enhancement provided by Stain SAN. Furthermore, we show that Stain SAN achieves results comparable with the state-of-the-art generative adversarial network-based approach without requiring separate training for stain adaptation or access to the target domain during training. Stain SAN’s performance is on par with HistAuGAN, proving its effectiveness and computational efficiency.ConclusionsStain SAN emerges as a promising solution, addressing the potential shortcomings of contemporary stain adaptation methods. Its effectiveness is underscored by notable improvements in the context of clinical estrogen receptor status classification, where it achieves the best AUC performance. The findings endorse Stain SAN as a robust approach for stain domain adaptation in histopathology images, with implications for advancing computational tasks in the field.

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Proceedings Article | 18 March 2019 Paper

Joint region and nucleus segmentation for characterization of tumor infiltrating lymphocytes in breast cancer

Mohamed Amgad, Anindya Sarkar, Chukka Srinivas, Rachel Redman, Simrath Ratra, Charles Bechert, Benjamin Calhoun, Karen Mrazeck, Uday Kurkure, Lee A. Cooper, Michael Barnes

Proceedings Volume 10956, 109560M (2019) https://doi.org/10.1117/12.2512892

KEYWORDS: Machine learning, Artificial intelligence, Image segmentation, Tumors, Breast cancer, Tissues, Pathology, Calibration, RGB color model, Data modeling, Medicine, Diagnostics

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