Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets

Although 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.This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732111 (PICCOLO project)

NewsMeSH: a new classifier designed to annotate health news with MeSH headings

Motivation In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. Methods We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. Results A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. Conclusions The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar

In search of an evidence-based strategy for quality assessment of human tissue samples: report of the tissue Biospecimen Research Working Group of the Spanish Biobank Network

The purpose of the present work is to underline the importance of obtaining a standardized procedure to ensure and evaluate both clinical and research usability of human tissue samples. The study, which was carried out by the Biospecimen Science Working Group of the Spanish Biobank Network, is based on a general overview of the current situation about quality assurance in human tissue biospecimens. It was conducted an exhaustive review of the analytical techniques used to evaluate the quality of human tissue samples over the past 30 years, as well as their reference values if they were published, and classified them according to the biomolecules evaluated: (i) DNA, (ii) RNA, and (iii) soluble or/and fixed proteins for immunochemistry. More than 130 publications released between 1989 and 2019 were analysed, most of them reporting results focused on the analysis of tumour and biopsy samples. A quality assessment proposal with an algorithm has been developed for both frozen tissue samples and formalin-fixed paraffin-embedded (FFPE) samples, according to the expected quality of sample based on the available pre-analytical information and the experience of the participants in the Working Group. The high heterogeneity of human tissue samples and the wide number of pre-analytic factors associated to quality of samples makes it very difficult to harmonize the quality criteria. However, the proposed method to assess human tissue sample integrity and antigenicity will not only help to evaluate whether stored human tissue samples fit for the purpose of biomarker development, but will also allow to perform further studies, such as assessing the impact of different pre-analytical factors on very well characterized samples or evaluating the readjustment of tissue sample collection, processing and storing procedures. By ensuring the quality of the samples used on research, the reproducibility of scientific results will be guaranteed.This work was funded by the Ministerio de Ciencia, Innovacion y Universidades of Spain and Instituto de Salud Carlos III (PI16/00528, PI16/00946, PI16/01207 and PI16/01276), co-funded by the Spanish Biobank Network (PT13/0010/0030, PT17/0015/0001, PT17/0015/0021, PT17/0015/0049, PT17/0015/0018, PT17/0015/0002, PT17/0015/0016, PT17/0015/0038, PT17/0015/0027, PT17/0015/0004, PT17/0015/0047, PT17/0015/0014, PT17/0015/0041, and PT17/0015/0006), European Regional Development Fund (FEDER) "A way to make Europe" and granted by Conselleria d'Innovacio, Recerca i Turisme del Govern de les Illes Balears (TEC/002/2017).S

Services Associated to Digitalised Contents of Tissues in Biobanks Across Europe: A Proof of Concept – BIOPOOL

Background: 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.European Commission's FP

A new tool to search tumor samples for research across Europe: BIOPOOL - Poster

Objective: 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.European Commission FP

In search of an evidence-based strategy for quality assessment of human tissue samples: report of the tissue biospecimen research working group of the spanish biobank network

The purpose of the present work is to underline the importance of obtaining a standardized procedure to ensure and evaluate both clinical and research usability of human tissue samples. The study, which was carried out by the Biospecimen Science Working Group of the Spanish Biobank Network, is based on a general overview of the cur‑ rent situation about quality assurance in human tissue biospecimens. It was conducted an exhaustive review of the analytical techniques used to evaluate the quality of human tissue samples over the past 30 years, as well as their reference values if they were published, and classifed them according to the biomolecules evaluated: (i) DNA, (ii) RNA, and (iii) soluble or/and fxed proteins for immunochemistry. More than 130 publications released between 1989 and 2019 were analysed, most of them reporting results focused on the analysis of tumour and biopsy samples. A qual‑ ity assessment proposal with an algorithm has been developed for both frozen tissue samples and formalin-fxed parafn-embedded (FFPE) samples, according to the expected quality of sample based on the available pre-analytical information and the experience of the participants in the Working Group. The high heterogeneity of human tissue samples and the wide number of pre-analytic factors associated to quality of samples makes it very difcult to harmo‑ nize the quality criteria. However, the proposed method to assess human tissue sample integrity and antigenicity will not only help to evaluate whether stored human tissue samples ft for the purpose of biomarker development, but will also allow to perform further studies, such as assessing the impact of diferent pre-analytical factors on very well characterized samples or evaluating the readjustment of tissue sample collection, processing and storing procedur