Overview of the ImageCLEFmed 2019 concept detection task

This paper describes the ImageCLEF 2019 Concept Detection Task. This is the 3rd edition of the medical caption task, after it was first proposed in ImageCLEF 2017. Concept detection from medical images remains a challenging task. In 2019, the format changed to a single subtask and it is part of the medical tasks, alongside the tuberculosis and visual question and answering tasks. To reduce noisy labels and limit variety, the data set focuses solely on radiology images rather than biomedical figures, extracted from the biomedical open access literature (PubMed Central). The development data consists of 56,629 training and 14,157 validation images, with corresponding Unified Medical Language System (UMLSR) concepts, extracted from the image captions. In 2019 the participation is higher, regarding the number of participating teams as well as the number of submitted runs. Several approaches were used by the teams, mostly deep learning techniques. Long short-term memory (LSTM) recurrent neural networks (RNN), adversarial auto-encoder, convolutional neural networks (CNN) image encoders and transfer learning-based multi-label classification models were the frequently used approaches. Evaluation uses F1-scores computed per image and averaged across all 10,000 test images

Medical image modality classification using discrete Bayesian Networks

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes

NLM at ImageCLEF 2017 caption task

This paper describes the participation of the U.S. National Library of Medicine (NLM) in the ImageCLEF 2017 caption task. We proposed different machine learning methods using training subsets that we selected from the provided data as well as retrieval methods using external data. For the concept detection subtask, we used Convolutional Neural Networks (CNNs) and Binary Relevance using decision trees for multi-label classification. We also proposed a retrieval-based approach using Open-i image search engine and MetaMapLite to recognize relevant terms and associated Concept Unique Identifiers (CUIs). For the caption prediction subtask, we used the recognized CUIs and the UMLS to generate the captions. We also applied Open-i to retrieve similar images and their captions. We submitted ten runs for the concept detection subtask and six runs for the caption prediction subtask. CNNs provided good results with regards to the size of the selected subsets and the limited number of CUIs used for training. Using the CUIs recognized by the CNNs, our UMLS-based method for caption prediction obtained good results with 0.2247 mean BLUE score. In both subtasks, the best results were achieved using retrieval-based approaches outperforming all submitted runs by all the participants with 0.1718 mean F1 score in the concept detection subtask and 0.5634 mean BLUE score in the caption prediction subtask

Overview of ImageCLEFcoral 2019 task

Understanding the composition of species in ecosystems on a large scale is key to developing effective solutions for marine conservation, hence there is a need to classify imagery automatically and rapidly. In 2019, ImageCLEF proposed for the first time the ImageCLEFcoral task. The task requires participants to automatically annotate and localize benthic substrate (such as hard coral, soft coral, algae and sponge) in a collection of images originating from a growing, large-scale dataset from coral reefs around the world as part of monitoring programmes. In its first edition, five groups participated submitting 20 runs using a variety of machine learning and deep learning approaches. Best runs achieved 0.24 in the annotation and localisation subtask and 0.04 on the pixel-wise parsing subtask in terms of MAP 0.5 IoU scores which measures the Mean Average Precision (MAP) when using the performance measure of Intersection over Union (IoU) bigger to 0.5 of the ground truth

Evaluating performance of biomedical image retrieval systems - an overview of the medical image retrieval task at ImageCLEF 2004-2013

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within the ImageCLEF benchmark in medical image retrieval and classification, a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created

Optimized voxel brain morphometry: association between brain volumes and the response to atypical antipsychotics

To date, few studies have addressed the relationship between brain structure alterations and responses to atypical antipsychotics in schizophrenia. To this end, in this study, magnetic resonance imaging (MRI) and voxel-based morphometry (VBM) were used to assess the relationship between the brain volumes of gray (GM) and white (WM) matters and the clinical response to risperidone or olanzapine in 30 schizophrenia patients. In comparison with healthy controls, the patients in this study showed a bilateral decrease in the anteromedial cerebellar hemispheres, the rectal gyrus and the insula, together with bilateral increases in GM in the basal ganglia. Both patient groups had a significantly smaller volume of WM in a region encompassing the internal and external capsules as compared to the controls. We found an inverse association between striatal size and the degree of clinical improvement, and a direct association between the degree of insular volume deficit and its improvement. The non-responder patient group showed a significant decrease in their left rectal gyrus as compared with the responder group. This study reveals a pattern of structural alterations in schizophrenia associated with the response to risperidone or olanzapine

General Overview of ImageCLEF at the CLEF 2015 Labs

This paper presents an overview of the ImageCLEF 2015 evaluation campaign, an event that was organized as part of the CLEF labs 2015. ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to databases of images in various usage scenarios and domains. In 2015, the 13th edition of ImageCLEF, four main tasks were proposed: 1) automatic concept annotation, localization and sentence description generation for general images; 2) identification, multi-label classification and separation of compound figures from biomedical literature; 3) clustering of x-rays from all over the body; and 4) prediction of missing radiological annotations in reports of liver CT images. The x-ray task was the only fully novel task this year, although the other three tasks introduced modifications to keep up relevancy of the proposed challenges. The participation was considerably positive in this edition of the lab, receiving almost twice the number of submitted working notes papers as compared to previous years