A deep learning–based algorithm for tall cell detection in papillary thyroid carcinoma

Introduction According to the World Health Organization, the tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC) comprising at least 30% epithelial cells two to three times as tall as they are wide. In practice, applying this definition is difficult causing substantial interobserver variability. We aimed to train a deep learning algorithm to detect and quantify the proportion of tall cells (TCs) in PTC. Methods We trained the deep learning algorithm using supervised learning, testing it on an independent dataset, and further validating it on an independent set of 90 PTC samples from patients treated at the Hospital District of Helsinki and Uusimaa between 2003 and 2013. We compared the algorithm-based TC percentage to the independent scoring by a human investigator and how those scorings associated with disease outcomes. Additionally, we assessed the TC score in 71 local and distant tumor relapse samples from patients with aggressive disease. Results In the test set, the deep learning algorithm detected TCs with a sensitivity of 93.7% and a specificity of 94.5%, whereas the sensitivity fell to 90.9% and specificity to 94.1% for non-TC areas. In the validation set, the deep learning algorithm TC scores correlated with a diminished relapse-free survival using cutoff points of 10% (p = 0.044), 20% (p < 0.01), and 30% (p = 0.036). The visually assessed TC score did not statistically significantly predict survival at any of the analyzed cutoff points. We observed no statistically significant difference in the TC score between primary tumors and relapse tumors determined by the deep learning algorithm or visually. Conclusions We present a novel deep learning–based algorithm to detect tall cells, showing that a high deep learning–based TC score represents a statistically significant predictor of less favorable relapse-free survival in PTC.Peer reviewe

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p <0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p <0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.Peer reviewe

An Overview of the Scala Programming Language

Scala fuses object-oriented and functional programming in a statically typed programming language. It is aimed at the construction of components and component systems. This paper gives an overview of the Scala language for readers who are familar with programming methods and programming language design

Interaction between unmanned vessels and COLREGs

Autonomous or remote controlled vessels are a completely new subject, which are under development right now. There is a possibility that in a few years we will see unmanned vessels sailing in international waters. These vessels will still have to be following the navigational rules at sea. This thesis examines unmanned vessels and how they will interact with the current amendment of the International Regulations for Preventing Collisions at Sea (COLREGS), mainly part A and B. The purpose of this thesis is to investigate how unmanned vessels interact with the navigational rules in COLREGS and if there will be any obstacles for implementing them in international waters. As research methodology we used a literature-based theoretical analysis in combination with a qualitative research. Our conclusions are that compliance with COLREGS doesn’t seem to be a huge problem for unmanned vessels. Most experts agree that compliance with COLREGS isn’t a serious issue and the liability in case something happens is a more serious problem. Unmanned vessels have already been tested in a few projects where they have been able to follow the Collision Regulations and navigate safely without accidents. However, a few things such as look-out and responsibility might need clarification before unmanned vessels will start to appear on a larger scale

HBTQ-Migration En kvalitativ studie om verksamheter i Europa som riktar sig till nyanlända hbtq-personer

The topic of this study is organizations in Europe that work with LGBT-refugees, -asylum seekers or other newcomers. This is a relatively new field in Europe, but some studies have been produced in mainly Canada, focusing at least partly on the same questions. The purpose of this study is to describe the situations this group is facing and also to illustrate in what ways organizations support this group. The methodology of this study is qualitative interviews with participants working at the organizations. The theories used for this study are different support categories and heteronormativity. Findings shows different needs in this group, that the organizations can play an important part in the lives of people in this group and how different kind of support are connected

Nationella minoriteter och det kommunala ansvaret: En fallstudie om Uddevalla kommun och den sverigefinska minoriteten

Minority languages has in a historical perspective been marginalised in Sweden. At the year of 2000 were 5 national minorities and their languages officially recognised as minority languages (Finnish, Meänkieli, Sami, Romani and Yiddish). Nine years later a Swedish law on minority rights was introduced (SFS 2009:724). The law put a large responsibility on municipalities to protect and promote the minority languages. The law also created an opportunity for municipalities to voluntarily become an administrative area for three of the minority languages (Finnish, Meänkieli and Sami). In the administrative areas are the minority rights strengthen. This study examines the case of Uddevalla municipality and the national minority Swedish Finns. This minority is struggling to retain their language and Uddevalla municipality has become an administrative area for the Finnish language. The purpose is to examine views of different types of work in the public administration who aims to strengthen and preserve the Finnish minority language. More specifically it focuses on implementation, accountability and the multicultural ideology which the minority rights framework is built upon. The data used in the study are both interviews with the Swedish minority representatives, public officials, politicians and official documents from Uddevalla municipality. Therefore, the method for the data collection in this study is qualitative interviews. The data were analysed by using a type of qualitative content analysis. The theories used are connected to implementation of political decisions but also to multiculturalism. Along with multiculturalism is also assimilation used as a theoretical concept. Findings shows that multicultural arguments weren’t used when Uddevalla became an administrative area for the Finnish language. Moreover, it identifies different types of important actors in the implementation of minority rights. The implementation is operated in a satisfying way, but there are organisational issues related to it. The responsibility of Uddevalla municipality for the minority is seen as part of a broader work with culture diversity and inclusion. It also shows strong views for the preservation of the Finnish language in the municipality

Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma

The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel method for training a convolutional neural network (CNN) algorithm for segmenting leukocytes in PTCs. Tissue samples from two retrospective PTC cohort were obtained and representative tissue slides from twelve patients were stained with hematoxylin and eosin (HE) and digitized. Then, the HE slides were destained and restained immunohistochemically (IHC) with antibodies to the pan-leukocyte anti CD45 antigen and scanned again. The two stain-pairs of all representative tissue slides were registered, and image tiles of regions of interests were exported. The image tiles were processed and the 3,3'-diaminobenzidine (DAB) stained areas representing anti CD45 expression were turned into binary masks. These binary masks were applied as annotations on the HE image tiles and used in the training of a CNN algorithm. Ten whole slide images (WSIs) were used for training using a five-fold cross-validation and the remaining two slides were used as an independent test set for the trained model. For visual evaluation, the algorithm was run on all twelve WSIs, and in total 238,144 tiles sized 500x500 pixels were analyzed. The trained CNN algorithm had an intersection over union of 0.82 for detection of leukocytes in the HE image tiles when comparing the prediction masks to the ground truth anti CD45 mask. We conclude that this method for generating antibody supervised annotations using the destain-restain IHC guided annotations resulted in high accuracy segmentations of leukocytes in HE tissue images.Peer reviewe

Supplementary_Table – Supplemental material for The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study

<p>Supplemental material, Supplementary_Table for The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study by Sebastian Stenman, Päivi Siironen, Harri Mustonen, Johan Lundin, Caj Haglund and Johanna Arola in Tumor Biology</p

External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining