# Deep Learning within a DICOM WSI Viewer for Histopathology
## 1 Introduction  
The practice of telemedicine, which provides healthcare to people all over the world, has been simplified by developments in computing and communication technologies [1; 2]. One of the key advances that is making this possible is the development of the Digital Imaging and Communications in Medicine (DICOM) standard. Medical images--the foundation of diagnosis--are stored and transmitted via the DICOM format [3]. The current version of the standard is 2022d, but changes are backward-compatible as the standard is normally updated five times per year. DICOM allows the exchange of images without internal image formats using metadata and defines imaging workflow information objects for images, patients' studies, reports, etc. Once an image object is defined by the standard, DICOM-compliant devices can interact with information systems to schedule diverse workflows. Although DICOM has been widely used in radiology, pathology was added later to its scope [4]. Despite the fact that DICOM promises imaging standards-based pathology, its clinical application is limited. No FDA-approved digital pathology system generatesDICOM natively; only one high-performance WSI diagnostic device has been approved for use in Asia and Europe. Furthermore, there is no IHE profile defined for microscopy, as is the case in radiology [5; 6], and the size of the WSI produced by digitizing microscope slides is much larger than images from radiology. Thus, a Picture Archiving and Communication System (PACS) will find it challenging to save and handle WSI images. To work with WSI samples, the DICOM WG26 (DICOM Pathology Working Group 26) defined two supplements, Supplement 122 and Supplement 145. Supplement 122 outlines how pathology specimen attributes are used in the pathology department imaging workflow to identify samples, and Supplement 145 describes how to use DICOM-standard messaging to store WSI attributes and images in PACSs. In this regard, a scalable and effective DICOM-WSI online viewer was proposed in [7] and demonstrated in several Digital Pathology Connectathons [8; 9]. Among other data management components, it consists of a PACS to store the WSI pictures and a DICOM-based web viewer. Recent research has proposed an automated DICOMization pipeline that can efficiently convert distinct proprietary microscope images from WSI scanners into standard DICOM with their biological information preserved in their metadata [10; 11], as well DICOM WSI visualization [12; 13] and other efficient architectures for telemedicine [14]. However, none of these works have demonstrated integration with both PACS and AI models yet. In addition to telemedicine, computer-aided diagnosis (CAD) systems are used to analyze and evaluate medical images [15]. CADs are used to highlight suspicious areas and measure anatomic structures to support professionals' decisions while analyzing an image. These systems combine AI, computer vision and image processing techniques. The use of AI in CAD systems has been validated in several studies [16; 17] in radiology, whereas the situation contrasts that of WSI. Although some CAD systems have been proposed in the literature for WSI images, including recent open-source software libraries providing custom [18; 19; 20], and generic end-to-end APIs for pathology image analysis, such as QuPath [21], PathML [19], HEAL [22], HistoCartography [23], CLAM [24] and TIAToolbox [25], to the best of our knowledge, none of them has been integrated in a DICOM environment. Only one development, EMPAIA, has tried to integrate all parts of the pathology ecosystem [26]. However, as the authors state, tests were carried out executing a very simple AI app and platform development is still ongoing. Other projects, such as Cytomine [27] or ASAP [28], lack PACS integration or state-of-the-art AI methods and other proposals have not been developed yet [29]. In this work, the integration of AI deep learning methods within a WSI web viewer that can be used in telemedicine and access stored WSI in DICOM format has been carried out.