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In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories., (© 2024. The Author(s).)

Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into "violent" versus "non-violent" method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into "violent" and "non-violent" suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods - both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed - hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into "violent" and "non-violent" methods, but on closer inspection "violent methods" can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes., (Copyright © 2019. Published by Elsevier Masson SAS.)

Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test.

Background: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Methods: Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results: Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. Conclusions: It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0). [ABSTRACT FROM AUTHOR]

We present a novel hybrid technique for improving the predictive performance of an online machine learning system: Combining advantages from both memory-based and concept-based procedures selective relearning tackles the problem of learning in gradually changing domains with delayed feedback. The idea is based on training and retraining the model only on the subsegment of the historical dataset which has been identified as the one most similar to the current conditions. We exemplify the effectiveness of our approach by evaluation in a well-known artificial dataset and show that selective relearning is rather insensitive to noise. Additionally, we present preliminary experimental results for a complex synthetic dataset resembling an online diagnostic system for the tile manufacturing industry and show that the procedure for selecting the best segment yields favorable training results in terms of the mean-squared error of the predictions. [ABSTRACT FROM AUTHOR]

(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning. [ABSTRACT FROM AUTHOR]