Spectral decoupling for training transferable neural networks in medical imaging

Many neural networks for medical imaging generalize poorly to data unseen during training. Such behavior can be caused by overfitting easy-to-learn features while disregarding other potentially informative features. A recent implicit bias mitigation technique called spectral decoupling provably encourages neural networks to learn more features by regularizing the networks' unnormalized prediction scores with an L2 penalty. We show that spectral decoupling increases the networks' robustness for data distribution shifts and prevents overfitting on easy-to-learn features in medical images. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on tissue slides and COVID-19 in chest radiographs. Networks trained with spectral decoupling achieve up to 9.5 percent point higher performance on external datasets. Spectral decoupling alleviates generalization issues associated with neural networks and can be used to complement or replace computationally expensive explicit bias mitigation methods, such as stain normalization in histological images.Peer reviewe

Spectral decoupling for training transferable neural networks in medical imaging

Many neural networks for medical imaging generalize poorly to data unseen during training. Such behavior can be caused by overfitting easy-to-learn features while disregarding other potentially informative features. A recent implicit bias mitigation technique called spectral decoupling provably encourages neural networks to learn more features by regularizing the networks' unnormalized prediction scores with an L2 penalty. We show that spectral decoupling increases the networks' robustness for data distribution shifts and prevents overfitting on easy-to-learn features in medical images. To validate our findings, we train networks with and without spectral decoupling to detect prostate cancer on tissue slides and COVID-19 in chest radiographs. Networks trained with spectral decoupling achieve up to 9.5 percent point higher performance on external datasets. Spectral decoupling alleviates generalization issues associated with neural networks and can be used to complement or replace computationally expensive explicit bias mitigation methods, such as stain normalization in histological images.Peer reviewe

Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy

Background To determine the added value of preoperative prostate multiparametric MRI (mpMRI) supplementary to clinical variables and their role in predicting post prostatectomy adverse findings and biochemically recurrent cancer (BCR). Methods All consecutive patients treated at HUS Helsinki University Hospital with robot assisted radical prostatectomy (RALP) between 2014 and 2015 were included in the analysis. The mpMRI data, clinical variables, histopathological characteristics, and follow-up information were collected. Study end-points were adverse RALP findings: extraprostatic extension, seminal vesicle invasion, lymph node involvement, and BCR. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and the Partin score were combined with any adverse findings at mpMRI. Predictive accuracy for adverse RALP findings by the regression models was estimated before and after the addition of MRI results. Logistic regression, area under curve (AUC), decision curve analyses, Kaplan-Meier survival curves and Cox proportional hazard models were used. Results Preoperative mpMRI data from 387 patients were available for analysis. Clinical variables alone, MSKCC nomogram or Partin tables were outperformed by models with mpMRI for the prediction of any adverse finding at RP. AUC for clinical parameters versus clinical parameters and mpMRI variables were 0.77 versus 0.82 for any adverse finding. For MSKCC nomogram versus MSKCC nomogram and mpMRI variables the AUCs were 0.71 and 0.78 for any adverse finding. For Partin tables versus Partin tables and mpMRI variables the AUCs were 0.62 and 0.73 for any adverse finding. In survival analysis, mpMRI-projected adverse RP findings stratify CAPRA and MSKCC high-risk patients into groups with distinct probability for BCR. Conclusions Preoperative mpMRI improves the predictive value of commonly used clinical variables for pathological stage at RP and time to BCR. mpMRI is available for risk stratification prebiopsy, and should be considered as additional source of information to the standard predictive nomograms.Peer reviewe

Associations of PTEN and ERG with Magnetic Resonance Imaging Visibility and Assessment of Non–organ-confined Pathology and Biochemical Recurrence After Radical Prostatectomy

Background: Diagnosing clinically significant prostate cancer (PCa) is challenging, but may be facilitated by biomarkers and multiparametric magnetic resonance imaging (MRI). Objective: To determine the association between biomarkers phosphatase and tensin homolog (PTEN) and ETS-related gene (ERG) with visible and invisible PCa lesions in MRI, and to predict biochemical recurrence (BCR) and non-organ-confined (non-OC) PCa by integrating clinical, MRI, and biomarker-related data. Design, setting, and participants: A retrospective analysis of a population-based cohort of men with PCa, who underwent preoperative MRI followed by radical prostatectomy (RP) during 2014-2015 in Helsinki University Hospital (n = 346), was conducted. A tissue microarray corresponding to the MRI-visible and MRI-invisible lesions in RP specimens was constructed and stained for PTEN and ERG. Outcome measurements and statistical analysis: Associations of PTEN and ERG with MRI-visible and MRI-invisible lesions were examined (Pearson's chi 2 test), and predictions of non-OC disease together with clinical and MRI parameters were determined (area under the receiver operating characteristic curve and logistic regression analyses). BCR prediction was analyzed by Kaplan-Meier and Cox proportional hazard analyses. Results and limitations: Patients with MRI-invisible lesions (n = 35) had less PTEN loss and ERG-positive expression compared with patients (n = 90) with MRI-visible lesions (17.2% vs 43.3% [p = 0.006]; 8.6% vs 20.0% [p = 0.125]). Patients with invisible lesions had better, but not statistically significantly improved, BCR-free survival probability in Kaplan-Meier analyses (p = 0.055). Rates of BCR (5.7% vs 21.1%; p = 0.039), extraprostatic extension (11.4% vs 44.6%; p < 0.001), seminal vesicle invasion (0% vs 21.1%; p = 0.003), and lymph node metastasis (0% vs 12.2%; p = 0.033) differed between the groups in favor of patients with MRI-invisible lesions. Biomarkers had no independent role in predicting non-OC disease or BCR. The short follow-up period was a limitation. Conclusions: PTEN loss, BCR, and non-OC RP findings were more often encountered with MRI-visible lesions. Patient summary: Magnetic resonance imaging (MRI) of the prostate misses some cancer lesions. MRI-invisible lesions seem to be less aggressive than MRI-visible lesions. (C) 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.Peer reviewe

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

Background: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. Methods: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. Results: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. Conclusions: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.Peer reviewe

Added Value of Multiparametric Magnetic Resonance Imaging in Men Undergoing Radical Prostatectomy

Prediction of the pathological T-stage (pT) in men undergoing radical prostatectomy (RP) is crucial for disease management as curative treatment is most likely when prostate cancer (PCa) is organ-confined (OC). Although multiparametric magnetic resonance imaging (MRI) has been shown to predict pT findings and the risk of biochemical recurrence (BCR), none of the currently used nomograms allow the inclusion of MRI variables. This study aims to assess the possible added benefit of MRI when compared to the Memorial Sloan Kettering, Partin table and CAPRA nomograms and a model built from available preoperative clinical variables. Logistic regression is used to assess the added benefit of MRI in the prediction of non-OC disease and Kaplan-Meier survival curves and Cox proportional hazards in the prediction of BCR. For the prediction of non-OC disease, all models with the MRI variables had significantly higher discrimination and net benefit than the models without the MRI variables. For the prediction of BCR, MRI prediction of non-OC disease separated the high-risk group of all nomograms into two groups with significantly different survival curves but in the Cox proportional hazards models the variable was not significantly associated with BCR. Based on the results, it can be concluded that MRI does offer added value to predicting non-OC disease and BCR, although the results for BCR are not as clear as for non-OC disease

Verkkosivujen kehitysmenetelmät

Insinöörityössä tutkittiin verkkosivujen kehitykseen liittyviä teknologioita ja keinoja, keinojen hyötyjä, heikkouksia sekä työmäärää. Myös eri keinojen monimutkaisuuteen perehdyttiin. Työssä pyrittiin löytämään paras mahdollinen tapa verkkosivujen hankkimiseen yksityishenkilölle. Työssä käytiin läpi neljä suosituinta sisällönhallintajärjestelmää, WordPress, Shopify, Wix ja SquareSpace. Valittujen järjestelmien hintoja, hyötyjä, heikkouksia ja käyttötarkoituksia vertailtiin, jotta saatiin valittua työn tarkoitukseen sopivin järjestelmä. Myös isännöintipalveluja tutkittiin, samoin verkkotunnuksia. Insinöörityössä rakennettiin sisällönhallintajärjestelmää hyödyntäen verkkosivut työn asiakkaana olleelle yksityishenkilölle. Sisällönhallintajärjestelmäksi valittiin WordPress. Verkkosivut toteutettiin asiakkaan toiveiden mukaan, ja verkkosivujen suunnittelu tehtiin yhdessä asiakkaan kanssa. Insinöörityön tuloksia voidaan hyödyntää, kun tarpeena on löytää paras mahdollinen keino verkkosivujen kehitykseen yksityishenkilölle, jolla ei ole aiempaa ohjelmointikokemusta

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Eurooppaa koetteleva energiakriisi on saanut sähkön hinnan nousemaan poikkeuksellisen korkeaksi, luonut suuria ja nopeita sähkön hinnan vaihteluita sekä tehnyt sähkömarkkinasta heikosti ennustettavan. Tämä yhdistettynä vaatimukseen kasvattaa uusiutuvan energian käyttöä, on johtanut tarpeeseen kehittää uuden tyyppisiä menetelmiä sähköverkon kulutuksen ja tuotannon tasapainottamiseksi. Lisäksi nopeasti muuttuva ja korkea sähkön hinta on luonut sähkön käyttäjille tarpeen seurata omaa sähkön kulutustaan reaaliajassa, jotta he voivat sopeuttaa kulutustaan suhteessa sähkön hintaan. Sähkön käytön ajoittamisen lisäksi vuoden 2023 alusta voimaan tulleen taseselvitysjakson sisäisen netotuksen hyödyntäminen voi tuottaa merkittävää taloudellista etua kohteissa, joissa on myös omaa sähkön pien-tuotantoa. Markkinoilla olevat mittaus- tai kuormaohjausjärjestelmät eivät kuitenkaan vielä hyödynnä taseselvitysjakson sisäistä netotusta sähkön kulutuksen ja tuotannon optimoinnissa. Tämä synnytti tarpeen tutkia, miten reaaliaikainen mittaus- ja laskentajärjestelmä voitaisiin toteuttaa. Koska tällä hetkellä käytössä olevat verkkoyhtiöiden sähkömittarit eivät toimita reaaliaikaista mittaustietoa, toteutettiin pilottikohteena toimineeseen omakotitaloon kaksisuuntainen energiamittaus. Tällä voitiin mitata reaaliajassa sähkön ostoa sähköverkosta ja sähkön myyntiä sähköverkkoon. Tämän lisäksi luotiin laskentaohjelmisto, joka laskee reaaliajassa sähkön tuotantoa, kulutusta ja sähkön kokonaishintaa käyttöpaikassa. Laskentaohjelmisto suorittaa taseselvitysjakson sisäisen netotuksen ja tuottaa käyttäjälle reaaliajassa laskentatuloksen arvioidusta sähkön euromääräisestä kokonaiskustannuksesta. Laskentaohjelmiston tulokset näytetään käyttäjälle mobiilisovelluksen välityksellä. Ratkaisussa laskentaohjelmistoon liitettiin ohjelmisto-rajapintojen välityksellä energiamittari, aurinkosähköjärjestelmän invertteri sekä pörssisähkön hinnan tarjoava tietojärjestelmä. Lopputuloksena syntyi toimiva mittausjärjestelmä ja laskentaohjelmisto, jonka laskentatulokset ovat seurattavissa mobiilisovelluksella. Lisäksi laskentaohjelmisto tuottaa rajapinnan, joka mahdollistaa pörssisähkön hinnan ja netotuslaskennan hyödyntämisen automaattisessa kuormaohjauksessa tai osana muita älykkäitä sähköjärjestelmiä

Additional file 1 of Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data

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