The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year's challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.

Neural networks, in the context of deep learning, show much promise in becoming an important tool with the purpose assisting medical doctors in disease detection during patient examinations. However, the current state of deep learning is something of a "black box", making it very difficult to understand what internal processes lead to a given result. This is not only true for non-technical users but among experts as well. This lack of understanding has led to hesitation in the implementation of these methods among mission-critical fields, with many putting interpretability in front of actual performance. Motivated by increasing the acceptance and trust of these methods, and to make qualified decisions, we present a system that allows for the partial opening of this black box. This includes an investigation on what the neural network sees when making a prediction, to both, improve algorithmic understanding, and to gain intuition into what pre-processing steps may lead to better image classification performance. Furthermore, a significant part of a medical expert's time is spent preparing reports after medical examinations, and if we already have a system for dissecting the analysis done by the network, the same tool can be used for automatic examination documentation through content suggestions. In this paper, we present a system that can look into the layers of a deep neural network and present the network's decision in a way that that medical doctors may understand. Furthermore, we present and discuss how this information can possibly be used for automatic reporting. Our initial results are very promising.

Automatic detection of diseases is a growing field of interest, and machine learning in form of deep learning neural networks are frequently explored as a potential tool for the medical video analysis. To both improve the "black box"-understanding and assist in the administrative duties of writing an examination report, we release an automated multimedia reporting software dissecting the neural network to learn the intermediate analysis steps, i.e., we are adding a new level of understanding and explainability by looking into the deep learning algorithms decision processes. The presented open-source software can be used for easy retrieval and reuse of data for automatic report generation, comparisons, teaching and research. As an example, we use live colonoscopy as a use case which is the gold standard examination of the large bowel, commonly performed for clinical and screening purposes. The added information has potentially a large value, and reuse of the data for the automatic reporting may potentially save the doctors large amounts of time.

In the future, medical doctors will to an increasing degree be assisted by deep learning neural networks for disease detection during examinations of patients. In order to make qualified decisions, the black box of deep learning must be opened to increase the understanding of the reasoning behind the decision of the machine learning system. Furthermore, preparing reports after the examinations is a significant part of a doctors work-day, but if we already have a system dissecting the neural network for understanding, the same tool can be used for automatic report generation. In this demo, we describe a system that analyses medical videos from the gastrointestinal tract. Our system dissects the Tensorflow-based neural network to provide insights into the analysis and uses the resulting classification and rationale behind the classification to automatically generate an examination report for the patient's medical journal.

Analysis of medical videos for detection of abnormalities and diseases requires both high precision and recall, but also real-time processing for live feedback and scalability for massive screening of entire populations. Existing work on this field does not provide the necessary combination of retrieval accuracy and performance.; [email protected] this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features. Furthermore, it is built in a modular way so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctors. Our experimental evaluation proves that our system has detection and localisation accuracy at least as good as existing systems for polyp detection, it is capable of detecting a wider range of diseases, it can analyze video in real-time, and it has a low resource consumption for scalability.

Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost impossible. In this paper, we present KVASIR, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (experienced endoscopists). In this respect, KVASIR is important for research on both single- and multi-disease computer aided detection. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.

Bowel preparation (cleansing) is considered to be a key precondition for successful colonoscopy (endoscopic examination of the bowel). The degree of bowel cleansing directly affects the possibility to detect diseases and may influence decisions on screening and follow-up examination intervals. An accurate assessment of bowel preparation quality is therefore important. Despite the use of reliable and validated bowel preparation scales, the grading may vary from one doctor to another. An objective and automated assessment of bowel cleansing would contribute to reduce such inequalities and optimize use of medical resources. This would also be a valuable feature for automatic endoscopy reporting in the future. In this paper, we present Nerthus, a dataset containing videos from inside the gastrointestinal (GI) tract, showing different degrees of bowel cleansing. By providing this dataset, we invite multimedia researchers to contribute in the medical field by making systems automatically evaluate the quality of bowel cleansing for colonoscopy. Such innovations would probably contribute to improve the medical field of GI endoscopy.