@article{AfonsoPereiraWeberetal., author = {Afonso, Luis Claudio Sugi and Pereira, Clayton R. and Weber, Silke A. T. and Hook, Christian and Falc{\~a}o, Alexandre X. and Papa, Joao Paulo}, title = {Hierarchical learning using deep optimum-path forest}, series = {Journal of Visual Communication and Image Representation}, volume = {71}, journal = {Journal of Visual Communication and Image Representation}, number = {August}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2020.102823}, abstract = {Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson's disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.}, language = {en} } @article{EbigboMendelScheppachetal., author = {Ebigbo, Alanna and Mendel, Robert and Scheppach, Markus W. and Probst, Andreas and Shahidi, Neal and Prinz, Friederike and Fleischmann, Carola and R{\"o}mmele, Christoph and G{\"o}lder, Stefan Karl and Braun, Georg and Rauber, David and R{\"u}ckert, Tobias and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm}, series = {Gut}, volume = {71}, journal = {Gut}, number = {12}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2021-326470}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-54293}, pages = {2388 -- 2390}, abstract = {In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63\% and 76\%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85\% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.}, language = {en} } @article{SouzaJrPalmMendeletal., author = {Souza Jr., Luis Antonio de and Palm, Christoph and Mendel, Robert and Hook, Christian and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Weber, Silke A. T. and Papa, Jo{\~a}o Paulo}, title = {A survey on Barrett's esophagus analysis using machine learning}, series = {Computers in Biology and Medicine}, volume = {96}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2018.03.014}, pages = {203 -- 213}, abstract = {This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{EbigboPalmProbstetal., author = {Ebigbo, Alanna and Palm, Christoph and Probst, Andreas and Mendel, Robert and Manzeneder, Johannes and Prinz, Friederike and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Siersema, Peter and Messmann, Helmut}, title = {A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology}, series = {Endoscopy International Open}, volume = {07}, journal = {Endoscopy International Open}, number = {12}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-1010-5705}, pages = {1616 -- 1623}, abstract = {The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.}, subject = {Diagnose}, language = {en} } @article{SouzaJrMendelStrasseretal., author = {Souza Jr., Luis Antonio de and Mendel, Robert and Strasser, Sophia and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box}, series = {Computers in Biology and Medicine}, volume = {135}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2021.104578}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-20126}, pages = {1 -- 14}, abstract = {Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.}, subject = {Deep Learning}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus}, series = {Gut}, volume = {69}, journal = {Gut}, number = {4}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2019-319460}, pages = {615 -- 616}, abstract = {Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9\% on 14 cases with neoplastic BE.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{EbigboMendelRueckertetal., author = {Ebigbo, Alanna and Mendel, Robert and R{\"u}ckert, Tobias and Schuster, Laurin and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Mende, Matthias and Steinbr{\"u}ck, Ingo and Faiss, Siegbert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Deprez, Pierre and Oyama, Tsuneo and Takahashi, Akiko and Seewald, Stefan and Sharma, Prateek and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of Artificial Intelligence: A pilot Study}, series = {Endoscopy}, volume = {53}, journal = {Endoscopy}, number = {09}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-1311-8570}, pages = {878 -- 883}, abstract = {Background and aims: The accurate differentiation between T1a and T1b Barrett's cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.}, subject = {Maschinelles Lernen}, language = {en} } @article{SouzaJrPassosMendeletal., author = {Souza Jr., Luis Antonio de and Passos, Leandro A. and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks}, series = {Computers in Biology and Medicine}, volume = {126}, journal = {Computers in Biology and Medicine}, number = {November}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2020.104029}, pages = {12}, abstract = {Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90\% of accuracy for the patch-based approach and 85\% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.}, subject = {Maschinelles Lernen}, language = {en} } @article{MendelRauberSouzaJretal., author = {Mendel, Robert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation}, series = {Computers in Biology and Medicine}, volume = {154}, journal = {Computers in Biology and Medicine}, number = {March}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2023.106585}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-57790}, pages = {13}, abstract = {Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student's prediction. The combined outputs of the students' prediction with the teachers' correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.}, language = {en} } @article{PassosSouzaJrMendeletal., author = {Passos, Leandro A. and Souza Jr., Luis Antonio de and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's esophagus analysis using infinity Restricted Boltzmann Machines}, series = {Journal of Visual Communication and Image Representation}, volume = {59}, journal = {Journal of Visual Communication and Image Representation}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2019.01.043}, pages = {475 -- 485}, abstract = {The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} }