Overview of the ImageCLEF 2015 medical classification task

This articles describes the ImageCLEF 2015 Medical Clas-sification task. The task contains several subtasks that all use a dataset of figures from the biomedical open access literature (PubMed Cen-tral). Particularly compound figures are targeted that are frequent inthe literature. For more detailed information analysis and retrieval it isimportant to extract targeted information from the compound figures.The proposed tasks include compound figure detection (separating com-pound from other figures), multi–label classification (define all sub typespresent), figure separation (find boundaries of the subfigures) and modal-ity classification (detecting the figure type of each subfigure). The tasksare described with the participation of international research groups inthe tasks. The results of the participants are then described and analysedto identify promising techniques

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks

The medical tasks in ImageCLEF have been run every year from 2004-2018 and many different tasks and data sets have been used over these years. The created resources are being used by many researchers well beyond the actual evaluation campaigns and are allowing to compare the performance of many techniques on the same grounds and in a reproducible way. Many of the larger data sets are from the medical literature, as such images are easier to obtain and to share than clinical data, which was used in a few smaller ImageCLEF challenges that are specifically marked with the disease type and anatomic region. This chapter describes the main results of the various tasks over the years, including data, participants, types of tasks evaluated and also the lessons learned in organizing such tasks for the scientific community

Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a LifeLog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on visual question answering where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign

Understanding Anatomy Classification Through Attentive Response Maps

One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions. In many applications, a simpler, less capable model that can be easily understood is favorable to a black-box model that has superior performance. In this paper, we present an approach for designing CNNs based on visualization of the internal activations of the model. We visualize the model's response through attentive response maps obtained using a fractional stride convolution technique and compare the results with known imaging landmarks from the medical literature. We show that sufficiently deep and capable models can be successfully trained to use the same medical landmarks a human expert would use. Our approach allows for communicating the model decision process well, but also offers insight towards detecting biases.Comment: Accepted at ISBI, 201

Overview of ImageCLEF 2017: Information extraction from images

This paper presents an overview of the ImageCLEF 2017 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2017. ImageCLEF is an ongoing initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in various usage scenarios and domains. In 2017, the 15th edition of ImageCLEF, three main tasks were proposed and one pilot task: (1) a LifeLog task about searching in LifeLog data, so videos, images and other sources; (2) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based on the figure alone; (3) a tuberculosis task that aims at detecting the tuberculosis type from CT (Computed Tomography) volumes of the lung and also the drug resistance of the tuberculosis; and (4) a remote sensing pilot task that aims at predicting population density based on satellite images. The strong participation of over 150 research groups registering for the four tasks and 27 groups submitting results shows the interest in this benchmarking campaign despite the fact that all four tasks were new and had to create their own community

Overview of the ImageCLEF 2016 Medical Task

ImageCLEF is the image retrieval task of the Conference and Labs of the Evaluation Forum (CLEF). ImageCLEF has historically focused on the multimodal and language–independent retrieval of images. Many tasks are related to image classification and the annotation of image data as well. The medical task has focused more on image retrieval in the beginning and then retrieval and classification tasks in subsequent years. In 2016 a main focus was the creation of meta data for a collection of medical images taken from articles of the the biomedical scientific literature. In total 8 teams participated in the four tasks and 69 runs were submitted. No team participated in the caption prediction task, a totally new task. Deep learning has now been used for several of the ImageCLEF tasks and by many of the participants obtaining very good results. A majority of runs was submitting using deep learning and this follows general trends in machine learning. In several of the tasks multimodal approaches clearly led to best results