Browsing by Author "Bilbao, Roberto"
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Item Autofluorescence image reconstruction and virtual staining for in-vivo optical biopsying(2021-02) Picon, Artzai; Medela, Alfonso; Sanchez-Peralta, Luisa F.; Cicchi, Riccardo; Bilbao, Roberto; Alfieri, Domenico; Elola, Andoni; Glover, Ben; Saratxaga, Cristina L.; COMPUTER_VISION; VISUALModern photonic technologies are emerging, allowing the acquisition of in-vivo endoscopic tissue imaging at a microscopic scale, with characteristics comparable to traditional histological slides, and with a label-free modality. This raises the possibility of an ‘optical biopsy’ to aid clinical decision making. This approach faces barriers for being incorporated into clinical practice, including the lack of existing images for training, unfamiliarity of clinicians with the novel image domains and the uncertainty of trusting ‘black-box’ machine learned image analysis, where the decision making remains inscrutable. In this paper, we propose a new method to transform images from novel photonics techniques (e.g. autofluorescence microscopy) into already established domains such as Hematoxilyn-Eosin (H-E) microscopy through virtual reconstruction and staining. We introduce three main innovations: 1) we propose a transformation method based on a Siamese structure that simultaneously learns the direct and inverse transformation ensuring domain back-transformation quality of the transformed data. 2) We also introduced an embedding loss term that ensures similarity not only at pixel level, but also at the image embedding description level. This drastically reduces the perception distortion trade-off problem existing in common domain transfer based on generative adversarial networks. These virtually stained images can serve as reference standard images for comparison with the already known H-E images. 3) We also incorporate an uncertainty margin concept that allows the network to measure its own confidence, and demonstrate that these reconstructed and virtually stained images can be used on previously-studied classification models of H-E images that have been computationally degraded and de-stained. The three proposed methods can be seamlessly incorporated on any existing architectures. We obtained balanced accuracies of 0.95 and negative predictive values of 1.00 over the reconstructed and virtually stained image-set on the detection of color-rectal tumoral tissue. This is of great importance as we reduce the need for extensive labeled datasets for training, which are normally not available on the early studies of a new imaging technology.Item Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets: Reducing the need of labeled data on biological datasets(IEEE, 2019-07-11) Medela, Alfonso; Picon, Artzai; Saratxaga, Cristina L.; Belar, Oihana; Cabezon, Virginia; Cicchi, Riccardo; Bilbao, Roberto; Glover, Ben; COMPUTER_VISION; VISUALAlthough deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training. Generating such extensive and well labelled datasets is time consuming and is not feasible for certain tasks and so, most of the medical datasets available are scarce in images and therefore, not enough for training. In this work we validate that the use of few shot learning techniques can transfer knowledge from a well defined source domain from Colon tissue into a more generic domain composed by Colon, Lung and Breast tissue by using very few training images. Our results show that our few-shot approach is able to obtain a balanced accuracy (BAC) of 90% with just 60 training images, even for the Lung and Breast tissues that were not present on the training set. This outperforms the finetune transfer learning approach that obtains 73% BAC with 60 images and requires 600 images to get up to 81% BAC.Item A new tool to search tumor samples for research across Europe: BIOPOOL - Poster(Springer, 2015) Sáiz, Mónica; Belar, Oihana; de Jong, Bas; Kap, Marcel; Bereciartua, Arantza; Ruiz, Rebeca; Viguri, Maria Amparo; Rezola, Ricardo; de Miguel, Eduardo; Saiz, Alberto; Fernández, Sara; Gaafar, Ayman; Catón, Blanca; Aguirre, Javier; Doukas, Michael; Muñoz, Elena; Gandon, Fabienne; Riegman, Peter; Bilbao, RobertoObjective: Searching tissues for research across biobanks and pathology departments is complicated due to the geographically dispersed distribution, diagnosis heterogeneity, language diversity and lack of online robust samples catalogues. BIOPOOL consortium (www.biopoolproject.eu) was created to give a solution based on a new search approach similar to Google images but focused on histological images and associated clinical databases. The project is funded by the 7FP of the European Commission (GA296162). Method: Digital images from colon, breast and lung cancer were used to develop the software. Eleven pathologists worked closely with IT developers. They defined the technical and functional requirements of the final system, and the identification and validation of the key visual features and regions of interest on the histological images and their associated data. Relevant visual descriptors were identified, coded and extracted automatically from the images in order to be included in the searching tool and retrieved later in the case it matches the query. Results: A new web-based search portal was developed to find tumor samples across biobanks based on query by image and/or text. The legal and ethical issues were taken in consideration. Conclusion: BIOPOOL network, is now open to biobanks and pathology departments to bring together, in a European scale, a research infrastructure that could help to conduct research through the synergy of medical knowledge and technology.Item PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets(Multidisciplinary Digital Publishing Institute (MDPI), 2020-11-28) Sánchez-Peralta, Luisa F.; Pagador, J. Blas; Picón, Artzai; Calderón, Ángel José; Polo, Francisco; Andraka, Nagore; Bilbao, Roberto; Glover, Ben; Saratxaga, Cristina L.; Sánchez-Margallo, Francisco M.Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for e_ective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four di_erent models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.Item Services Associated to Digitalised Contents of Tissues in Biobanks Across Europe: A Proof of Concept – BIOPOOL(ISBER Annual Meeting, 2013-05) de Jong, Bas; Belar, Oihana; Bereciartua, Arantza; Picon, Artzai; Muñoz, Elena; Sevilla, D.; Moscone, F.; Gandon, Fabienne; Tosseti, E.; García, S.; Riegman, Peter; Bilbao, RobertoBackground: Pathology departments and biobanks are increasingly using Digital Pathology (DP) images for sharing of research results, ring trials, education, fast second-opinion diagnostics, pathology panels, digital back-up of slides, image analysis algorithms, and etcetera. To fully exploit the potential of DP, the BIOPOOL project develops software for extracting and gathering DP slides with well defined associated data from multiple biobanks and pathology archives to create pools of images, as biobanks networks, on which clinicians and researchers can search for reference, score for similarities with their own images using an innovative Content Based Image Retrieval system, and perform indepth image analyses. Methods: The BIOPOOL Proof-of-Concept (PoC) with minimal, critical functionality serves as the basis on which the system will be further developed. For this PoC we are studying existing DP image formats and systems that could be of use, designed both PoC and end-phase validation plans and end-phase functional requirements. Results: For the PoC, only colon DP slides with associated data (normal and high grade carcinoma), digitalised on Hamamatsu and Olympus scanners, are used. Pathologists have assigned morphological areas of interest for image searching development and creation of the basic DPpool, which were both validated. Functional requirements include a user-interface for searching on textual and morphology aspects, multiscanner format support, storage capacity, computational power for search processing and IT equipment and support. Conclusions: The PoC model is a template for expanding the BIOPOOL system to full functionality. After final validation BIOPOOL may then serve as a leading example for using the full potential of DP imaging.