In this paper, we explain a typical deep learning system called “Deep Convolution Neural Network : DCNN”,which becomes to be de facto standard in the field of computer vision. Moreover, we also explain an application a DCNN into the medical image analysis for the computer aided diagnosis.
Considering the balance between sample size and the number of weight is a important factor for developing a deep learning system, however, acquiring the data is hard task in the field of medical imaging.
Thus, we introduce a kind of transfer learning method into the DCNN. As the result, we confirm the improvement of classification performance for the diffuse lung disease （DLD） patterns.

Medical imaging in clinical trials is the essential surrogate endpoint for evaluation of drug efficacy and safety.
Imaging core laboratory builds the comprehensive process of imaging evaluation under the guideline such as Food and Drug Administration（FDA）guidance. DICOM data collection has a rule of evidence-based condition for same image quality by the medical imaging devices such as CT, MRI, PET, SPECT, and the US. DICOM de-identification for clinical trials guided by DICOM standard（The DICOM standards committee Working Group 18（WG18）wrote Supplement 142）.
Detailed DICOM masking rule is identified in this DICOM standard supplement such as de-identification,pseudonymization, and anonymization. Various criteria represented such as Response Evaluation Criteria in Solid Tumors （RECIST）1.1 for drug efficacy and safety have introduced with recently advanced computer assisted lesion management software systems. These big data in imaging clinical trials are managed not only local PACS but also cloud imaging storage and managing with Vendor Neutral Archiving（VNA）and Quality Management Systems（QMS）system.
In this educational paper, we introduce the advanced workflow of evaluation of drug efficacy and safety with application technique of medical imaging and IT cloud solutions.

Digital phase-contrast-imaging（phase-imaging）using a small-focus X-ray tube provides images with greater sharpness than conventional X-ray imaging（conventional-imaging）, because the phase-imaging can produce images that are edge-enhanced at the boundary of an object because of refracted X-rays. In the present study, by performing image processing on a conventional-image we investigated whether we can produce an edge-enhanced conventional-image of equal edge-enhancement effect to that of phase-imaging. We used unsharp masking and Laplacian filtering as post-processing for edge enhancement of conventional-images. To determine the image processing parameters, the profile curves of acrylic fibers were used. Using these optimal imaging parameters, image processing was done on conventional-images of acrylic phantoms, followed by image quality comparisons of the post-processed conventional-images（post-processed-images）and phase-images. Furthermore, we performed a frequency analysis of the phase-image, conventional-image, and post-processedimage. Edge-enhanced conventional-images were obtained with a similar but slightly lower edge-enhancement effect than phase-images by processing images with appropriate parameters for unsharp masking and for Laplacian filtering. However,an increase in noise occurred because of the edge-enhancement processing. Edge-enhanced conventional-images of similar edge-enhancement effect to phase-images are obtainable by image processing of conventional-images. However, the edge clarity of the post-processed-images is somewhat worse than that of phase-images. Moreover, the edge-enhancement effect of post-processed-images is far lower than that of phase-images, because of increased noise resulting from the image processing.