A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Machine learning methods for the analysis and interpretation of images and other multi-dimensional data

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Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Diagnostic Significance of Exosomal miRNAs in the Plasma of Breast Cancer Patients

Poster Session AbstractsBackground and Aims: Emerging evidence that microRNAs (miRNAs) play an important role in cancer development has opened up new opportunities for cancer diagnosis. Recent studies demonstrated that released exosomes which contain a subset of both cellular mRNA and miRNA could be a useful source of biomarkers for cancer detection. Here, we aim to develop a novel biomarker for breast cancer diagnosis using exosomal miRNAs in plasma. Methods: We have developed a rapid and novel isolation protocol to enrich tumor-associated exosomes from plasma samples by capturing tumor specific surface markers containing exosomes. After enrichment, we performed miRNA profiling on four sample sets; (1) Ep-CAM marker enriched plasma exosomes of breast cancer patients; (2) breast tumors of the same patients; (3) adjacent non-cancerous tissues of the same patients; (4) Ep-CAM marker enriched plasma exosomes of normal control subjects. Profiling is performed using PCR-based array with human microRNA panels that contain more than 700 miRNAs. Results: Our profiling data showed that 15 miRNAs are concordantly up-regulated and 13 miRNAs are concordantly down-regulated in both plasma exosomes and corresponding tumors. These account for 25% (up-regulation) and 15% (down-regulation) of all miRNAs detectable in plasma exosomes. Our findings demonstrate that miRNA profile in EpCAM-enriched plasma exosomes from breast cancer patients exhibit certain similar pattern to that in the corresponding tumors. Based on our profiling results, plasma signatures that differentiated breast cancer from control are generated and some of the well-known breast cancer related miRNAs such as miR-10b, miR-21, miR-155 and miR-145 are included in our panel list. The putative miRNA biomarkers are validated on plasma samples from an independent cohort from more than 100 cancer patients. Further validation of the selected markers is likely to offer an accurate, noninvasive and specific diagnostic assay for breast cancer. Conclusions: These results suggest that exosomal miRNAs in plasma may be a novel biomarker for breast cancer diagnosis.link_to_OA_fulltex