Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future

Computer-aided detection of early Barrett's cancer

New automatic techniques for detection of esophageal cancer rival top medical specialist

Novel developments in endoscopic mucosal imaging

\u3cp\u3eEndoscopic techniques such as high-definition and optical-chromoendoscopy have had enormous impact on endoscopy practice. Since these techniques allow assessment of most subtle morphological mucosal abnormalities, further improvements in endoscopic practice lay in increasing the detection efficacy of endoscopists. Several new developments could assist in this. First, web based training tools could improve the skills of the endoscopist for enhancing the detection and classification of lesions. Secondly, incorporation of computer aided detection will be the next step to raise endoscopic quality of the captured data. These systems will aid the endoscopist in interpreting the increasing amount of visual information in endoscopic images providing real-time objective second reading. In addition, developments in the field of molecular imaging open opportunities to add functional imaging data, visualizing biological parameters, of the gastrointestinal tract to white-light morphology imaging. For the successful implementation of abovementioned techniques, a true multi-disciplinary approach is of vital importance.\u3c/p\u3

A learning system for cancer detection

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Real-time semantic context labeling for image understanding

The use of context information in a scene is an important aid for full semantic scene understanding in security and surveillance applications. To this end, this paper presents an innovative semantic context-labeling algorithm for three context classes, trading-off quality and real-time execution. Our system consists of three consecutive stages: image segmentation, region-based feature extraction and classification. We propose the joint use of the features color in HSV space, texture from Gabor filters and spatial context, in combination with the Directional Nearest Neighbor (DNN) method for constructing the undirected graph for segmentation. Compared to recent literature, this combination is over 35 times faster and achieves a coverability rate that is 65% higher

Automatic detection of early esophageal cancer with CNNS using transfer learning

The incidence of Esophageal Adenocarcinoma (EAC), a form of esophageal cancer, has rapidly increased in recent years. Dysplastic tissue can be removed endoscopically at an early stage, and since survival chances of patients are limited at later stages of the disease, early detection is of key impor- tance. Recently, several CAD systems for HD endoscopic images have been proposed, but these are computationally expensive, making them unfit for clinical use requiring real- time analysis. In this paper, we present a novel approach for early esophageal cancer detection using Transfer Learning with CNNs. Given the small amount of annotated data, CNN Codes are applied, where intermediate layers of the net- work are used as features for conventional classifiers. Various classifiers are combined with four of the most widely-used networks. Additionally, sliding windows are used to obtain a coarse-grained annotation indicating any possible cancerous regions. This approach outperforms the current state-of-the-art with a frame-based AUC of 0.92, while allowing both near real-time prediction and annotation at 2 fps, in a MATLAB-based framework

Multi-modal classification of polyp malignancy using CNN features with balanced class augmentation

Colorectal polyps are an indicator of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models albeit with limited success. In this paper, a classification approach is proposed to differentiate between polyp malignancy, using features extracted from the Global Average Pooling (GAP) layer of a Convolutional Neural Network (CNNs). Two recent endoscopic modalities are used to improve the algorithm prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle an unbalanced class distribution and to improve the decision of the classifiers. As a result, we increase the performance compared to state-of-the-art methods (0.97 vs 0.90 AUC). Our method for automatic polyp malignancy classification facilitates future advances towards patient safety and may avoid time-consuming and costly histopathological assessment