Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study

Autor: Mohammad H. Jafari, Golara Javadi, Antonio Hurtado, Silvia D. Chang, Claudia Kesch, Peter C. Black, Sharareh Bayat, Samira Sojoudi, Purang Abolmaesumi, Mehran Pesteie, Samareh Samadi, Parvin Mousavi
Rok vydání: 2019
Předmět:
Image-Guided Biopsy
Male
Prostate biopsy
Computer science
0206 medical engineering
Biomedical Engineering
Health Informatics
02 engineering and technology
Sensitivity and Specificity
Synthetic data
030218 nuclear medicine & medical imaging
03 medical and health sciences
Prostate cancer
0302 clinical medicine
Prostate
Biopsy
medicine
Humans
Radiology
Nuclear Medicine and imaging
Sensitivity (control systems)
Invariant (computer science)
Ultrasonography
Interventional
medicine.diagnostic_test
business.industry
Prostatic Neoplasms
Pattern recognition
General Medicine
Pathology Report
medicine.disease
020601 biomedical engineering
Computer Graphics and Computer-Aided Design
3. Good health
Computer Science Applications
medicine.anatomical_structure
Feasibility Studies
Surgery
Computer Vision and Pattern Recognition
Artificial intelligence
Neural Networks
Computer
business
Zdroj: International journal of computer assisted radiology and surgery. 15(6)
ISSN: 1861-6429
Popis: Ultrasound imaging is routinely used in prostate biopsy, which involves obtaining prostate tissue samples using a systematic, yet, non-targeted approach. This approach is blinded to individual patient intraprostatic pathology, and unfortunately, has a high rate of false negatives. In this paper, we propose a deep network for improved detection of prostate cancer in systematic biopsy. We address several challenges associated with training such network: (1) Statistical labels: Since biopsy core’s pathology report only represents a statistical distribution of cancer within the core, we use multiple instance learning (MIL) networks to enable learning from ultrasound image regions associated with those data; (2) Limited labels: The number of biopsy cores are limited to at most 12 per patient. As a result, the number of samples available for training a deep network is limited. We alleviate this issue by effectively combining Independent Conditional Variational Auto Encoders (ICVAE) with MIL. We train ICVAE to learn label-invariant features of RF data, which is subsequently used to generate synthetic data for improved training of the MIL network. Our in vivo study includes data from 339 prostate biopsy cores of 70 patients. We achieve an area under the curve, sensitivity, specificity, and balanced accuracy of 0.68, 0.77, 0.55 and 0.66, respectively. The proposed approach is generic and can be applied to several other scenarios where unlabeled data and noisy labels in training samples are present.
Databáze: OpenAIRE
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