Overview of ImageCLEFmedical 2022 – Caption Prediction and Concept Detection

The 2022 ImageCLEFmedical caption prediction and concept detection tasks follow similar challenges that were already run from 2017–2021. The objective is to extract Unified Medical Language System (UMLS) concept annotations and/or captions from the image data that are then compared against the original text captions of the images. The images used for both tasks are a subset of the extended Radiology Objects in COntext (ROCO) data set which was used in ImageCLEFmedical 2020. In the caption prediction task, lexical similarity with the original image captions is evaluated with the BiLingual Evaluation Understudy (BLEU) score. In the concept detection task, UMLS terms are extracted from the original text captions, combined with manually curated concepts for image modality and anatomy, and compared against the predicted concepts in a multi-label way. The F1-score was used to assess the performance. The task attracted a strong participation with 20 registered teams. In the end, 12 teams submitted 157 graded runs for the two subtasks. Results show that there is a variety of techniques that can lead to good prediction results for the two tasks. Participants used image retrieval systems for both tasks, while multi-label classification systems were used mainly for the concept detection, and Transformer-based architectures primarily for the caption prediction subtask

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP

ImageCLEF 2022: Multimedia Retrieval in Medical, Nature, Fusion, and Internet Applications

ImageCLEF is part of the Conference and Labs of the Evaluation Forum (CLEF) since 2003. CLEF 2022 will take place in Bologna, Italy. ImageCLEF is an ongoing evaluation initiative which promotes the evaluation of technologies for annotation, indexing, and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In its 20th edition, ImageCLEF will have four main tasks: (i) a Medical task addressing concept annotation, caption prediction, and tuberculosis detection; (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images; (iii) an Aware task addressing the prediction of real-life consequences of online photo sharing; and (iv) a new Fusion task addressing late fusion techniques based on the expertise of the pool of classifiers. In 2021, over 100 research groups registered at ImageCLEF with 42 groups submitting more than 250 runs. These numbers show that, despite the COVID-19 pandemic, there is strong interest in the evaluation campaign

Overview of ImageCLEFmedical 2022 ::caption prediction and concept detection

The 2022 ImageCLEFmedical caption prediction and concept detection tasks follow similar challenges that were already run from 2017–2021. The objective is to extract Unified Medical Language System (UMLS) concept annotations and/or captions from the image data that are then compared against the original text captions of the images. The images used for both tasks are a subset of the extended Radiology Objects in COntext (ROCO) data set which was used in ImageCLEFmedical 2020. In the caption prediction task, lexical similarity with the original image captions is evaluated with the BiLingual Evaluation Understudy (BLEU) score. In the concept detection task, UMLS terms are extracted from the original text captions, combined with manually curated concepts for image modality and anatomy, and compared against the predicted concepts in a multi-label way. The F1-score was used to assess the performance. The task attracted a strong participation with 20 registered teams. In the end, 12 teams submitted 157 graded runs for the two subtasks. Results show that there is a variety of techniques that can lead to good prediction results for the two tasks. Participants used image retrieval systems for both tasks, while multi-label classification systems were used mainly for the concept detection, and Transformer-based architectures primarily for the caption prediction subtask

The NanoDefine Methods Manual

The NanoDefine Methods Manual has been developed within the NanoDefine project 'Development of an integrated approach based on validated and standardized methods to support the implementation of the EC recommendation for a definition of nanomaterial', funded by the European Union's 7th Framework Programme, under grant agreement 604347. The overall goal of the NanoDefine project was to support the implementation of the European Commission Recommendation on the definition of nanomaterial (2011/696/EU). The project has developed an integrated empirical approach, which allows identifying a material as a nano- or not a nanomaterial according to the EC Recommendation. The EC Recommendation for a definition of nanomaterial provides a common basis for regulatory purposes across all areas of European Union policy. The definition or core parts of it have been enacted in EU legislation, (e.g. REACH, Biocidal Products Regulation, Medical Devices Regulation). Therefore, development of appropriate methods and approaches to understand if a material meets the criteria laid down in the EC NM Definition is of key importance for both industry, stakeholders and regulators. The NanoDefine Methods Manual aims at providing guidance through the nanomaterial characterization process, on the use of the characterization methods as well as their application range and their limits to assists the user in choosing the most appropriate measurement method(s) to identify any substance according to the EC recommendation for a definition of nanomaterial. The NanoDefine Methods Manual consists of three parts. Part 1: covers the NanoDefiner framework. It includes short introduction to the NanoDefine Framework and summarizes the results of a comprehensive study of the available Characterisation Methods (CMs) which were candidates for a reliable analysis of the number based size distribution of a particulate material, with the goal to identify nanomaterials according to the EC NM Definition. The report presents the Decision Support Flow Scheme, which guides the user logically through a sequence of tasks, decision nodes and options in order to decide whether a material is a nanomaterial according to the EC NM Definition. Finally the report introduce the NanoDefiner e–tool which implements all tools developed within the NanoDefine framework in a software and assists the user in the decision process whether a material is a nanomaterial according to the EC NM Definition. Part 2: discusses the outcome of the evaluation of the nanomaterials characterisation methods. The document is based on the results of a comprehensive study performed within the NanoDefine project on the available Characterisation Methods (CMs) which are candidates for performing a reliable analysis of the number-based size distribution of a particulate material, with the goal to identify nanomaterials according to the European Commission recommendation on the definition of nanomaterial. This report discusses most available size characterisation methods for nanomaterials. Different types of methods that allow for the determination of size and size distributions are described and an overview of techniques and their capabilities is presented in the form of detailed, user-friendly tables. Part 3: gathers all Standard Operating Procedures (SOPs) developed within the NanoDefine project (available at the editorial deadline of the Manual) for nanomaterial characterisation. The aim of this document is to present SOPs developed within NanoDefine project to facilitated and harmonised the particle size distribution measurements. The presented SOPs are detailed, material/method specific protocols designed to produce liquid dispersions of the NanoDefine priority substances such that the resulting dispersions are stable and contain only or mainly primary constituent particles. All SOPs are presented in the document as standalone, self-explanatory documents which then can be easily extracted from the report. Special attention is given to the sonication issue, as it seems to be one of the most challenging steps in the sample preparation. The NanoDefine Methods Manual is available in a form of three separated Reports or in a special format of a full collection.JRC.F.2-Consumer Products Safet

Static and Dynamic Accuracy and Occlusion Robustness of SteamVR Tracking 2.0 in Multi-Base Station Setups

The tracking of objects and person position, orientation, and movement is relevant for various medical use cases, e.g., practical training of medical staff or patient rehabilitation. However, these demand high tracking accuracy and occlusion robustness. Expensive professional tracking systems fulfill these demands, however, cost-efficient and potentially adequate alternatives can be found in the gaming industry, e.g., SteamVR Tracking. This work presents an evaluation of SteamVR Tracking in its latest version 2.0 in two experimental setups, involving two and four base stations. Tracking accuracy, both static and dynamic, and occlusion robustness are investigated using a VIVE Tracker (3.0). A dynamic analysis further compares three different velocities. An error evaluation is performed using a Universal Robots UR10 robotic arm as ground-truth system under nonlaboratory conditions. Results are presented using the Root Mean Square Error. For static experiments, tracking errors in the submillimeter and subdegree range are achieved by both setups. Dynamic experiments achieved errors in the submillimeter range as well, yet tracking accuracy suffers from increasing velocity. Four base stations enable generally higher accuracy and robustness, especially in the dynamic experiments. Both setups enable adequate accuracy for diverse medical use cases. However, use cases demanding very high accuracy should primarily rely on SteamVR Tracking 2.0 with four base stations

Overview of the ImageCLEF 2022 : multimedia retrieval in medical, social media and nature applications

This paper presents an overview of the ImageCLEF 2022 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2022. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2022, the 20th edition of ImageCLEF runs four main tasks: (i) a medical task that groups two previous tasks, i.e., caption analysis and tuberculosis prediction, (ii) a social media aware task on estimating potential real-life effects of online image sharing, (iii) a nature coral task about segmenting and labeling collections of coral reef images, and (iv) a new fusion task addressing the design of late fusion schemes for boosting the performance, with two real-world applications: image search diversification (retrieval) and prediction of visual interestingness (regression). The benchmark campaign received the participation of over 25 groups submitting more than 258 runs