Drukker, Karen Chen, Weijie Gichoya, Judy Gruszauskas, Nicholas Kalpathy-Cramer, Jayashree Koyejo, Sanmi Myers, Kyle Sá, Rui C Sahiner, Berkman Whitney, Heather
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Published in
Journal of medical imaging (Bellingham, Wash.)
To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detec...
Kim, Hyeongseok Lee, Seoyoung Shim, Woo Jung Choi, Min-Seong Cho, Seungryong
Published in
Journal of medical imaging (Bellingham, Wash.)
Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation methods to reduce data discrepancies between different data domains, detailed analysis for explaining the perfo...
Pu, Lucas Leader, Joseph K Ali, Alaa Geng, Zihan Wilson, David
Published in
Journal of medical imaging (Bellingham, Wash.)
Lung transplantation is the standard treatment for end-stage lung diseases. A crucial factor affecting its success is size matching between the donor's lungs and the recipient's thorax. Computed tomography (CT) scans can accurately determine recipient's lung size, but donor's lung size is often unknown due to the absence of medical images. We aim t...
Tivnan, Matthew Gang, Grace J. Wang, Wenying Noël, Peter Sulam, Jeremias Webster Stayman, J.
Published in
Journal of Medical Imaging
Optimization of CT image quality typically involves balancing variance and bias. In traditional filtered back-projection, this trade-off is controlled by the filter cutoff frequency. In model-based iterative reconstruction, the regularization strength parameter often serves the same function. Deep neural networks (DNNs) typically do not provide thi...
Fakhrzadeh, Azadeh Karimian, Pouya Meyari, Mahsa Hendriks, Cris L. Luengo Holm, Lena Sonne, Christian Dietz, Rune Spörndly-Nees, Ellinor
There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. Automated methods are necessary tools in the quantitative assessment of histopathology to overcome the s...
Wong, Koon-Pong Homer, Suzanne Y. Wei, Sindy H. Yaghmai, Nazanin Estrada Paz, Oscar A. Young, Timothy J. Buhr, Russell G. Barjaktarevic, Igor Shrestha, Liza Daly, Morgan
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Published in
Journal of Medical Imaging
Purpose To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice. Approach In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built ...
Riveira-Martin, Mercedes Rodríguez-Ruiz, Alejandro Martí, Robert Chevalier, Margarita
Published in
Journal of Medical Imaging
Purpose Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Art...
Yu, Xiaojun Ge, Chenkun Li, Mingshuai Aziz, Muhammad Zulkifal Mo, Jianhua Fan, Zeming
Published in
Journal of Medical Imaging
Purpose Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease...
Hooper, Sarah M. Wu, Sen Davies, Rhodri H. Bhuva, Anish Schelbert, Erik B. Moon, James C. Kellman, Peter Xue, Hui Langlotz, Curtis Ré, Christopher
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Published in
Journal of Medical Imaging
Purpose Neural networks have potential to automate medical image segmentation but require expensive labeling efforts. While methods have been proposed to reduce the labeling burden, most have not been thoroughly evaluated on large, clinical datasets or clinical tasks. We propose a method to train segmentation networks with limited labeled data and ...
Applegate, Matthew B. Kose, Kivanc Ghimire, Sandesh Rajadhyaksha, Milind Dy, Jennifer
Published in
Journal of Medical Imaging
Purpose Deep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits their utility. Here, we develop an algorithm (noise2Nyquist) that leverages the fact that Nyquist sampling...
