Genome-wide association studies (GWASs) have identified over 2,400 genetic variants associated to breast cancer. Conventional GWASs methods that analyze variants independently often overlook the complex genetic interactions underlying disease susceptibility. Recent advancements such as Machine learning and deep learning approaches present promising alternatives, yet encounter challenges, including overfitting due to high dimensionality (∼10 million variants) and limited sample sizes, as well as limited interpretability. Here, we present GenoGraph, a graph-based contrastive learning framework designed to address these limitations by modeling high-dimensional genetic data in low-sample-size scenarios. We demonstrate GenoGraph's efficacy in breast cancer case-control classification task, achieving accuracy of 0.96 using the Biobank of Eastern Finland dataset. GenoGraph identified rs11672773 as a key risk variant in Finnish population, with significant interactions with rs10759243 and rs3803662. Furthermore, in-silico validation confirmed the biological relevance of these findings, underscoring GenoGraph's potential to advance breast cancer risk prediction and inform genetic interaction discoveries within population-specific contexts, with future extensions toward personalized medicine.

BACKGROUND: The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the confidence in the predictions. METHODS: We propose a semantic segmentation model using Bayesian representation to segment nuclei from the histopathology images and to further quantify the epistemic uncertainty. We employ Bayesian approximation with Monte-Carlo (MC) dropout during the inference time to estimate the model's prediction uncertainty. RESULTS: We evaluate the performance of the proposed approach on the PanNuke dataset, which consists of 312 visual fields from 19 organ types. We compare the nuclei segmentation accuracy of our approach with that of a fully convolutional neural network, U-Net, SegNet, and the state-of-the-art Hover-net. We use F1-score and intersection over union (IoU) as the evaluation metrics. The proposed approach achieves a mean F1-score of 0.893 ± 0.008 and an IoU value of 0.868 ± 0.003 on the test set of the PanNuke dataset. These results outperform the Hover-net, which has a mean F1-score of 0.871 ± 0.010 and an IoU value of 0.840 ± 0.032. CONCLUSIONS: The proposed approach, which incorporates Bayesian representation and Monte-Carlo dropout, demonstrates superior performance in segmenting nuclei from histopathology images compared to existing models such as U-Net, SegNet, and Hover-net. By considering the epistemic uncertainty, our model provides a more reliable estimation of the prediction confidence. These findings highlight the potential of Bayesian deep learning for improving medical image analysis tasks and can contribute to the development of more accurate and reliable computer-aided diagnostic systems.

We propose a unified approach to automatic foreign accent recognition. It takes advantage of recent technology advances in both linguistics and acoustics based modeling techniques in automatic speech recognition (ASR) while overcoming the issue of a lack of a large set of transcribed data often required in designing state-of-the-art ASR systems. The key idea lies in defining a common set of fundamental units “universally” across all spoken accents such that any given spoken utterance can be transcribed with this set of “accent-universal” units. In this study, we adopt a set of units describing manner and place of articulation as speech attributes. These units exist in most spoken languages and they can be reliably modeled and extracted to represent foreign accent cues. We also propose an i-vector representation strategy to model the feature streams formed by concatenating these units. Testing on both the Finnish national foreign language certificate (FSD) corpus and the English NIST 2008 SRE corpus, the experimental results with the proposed approach demonstrate a significant system performance improvement with p-value over those with the conventional spectrum-based techniques. We observed up to a 15% relative error reduction over the already very strong i-vector accented recognition system when only manner information is used. Additional improvement is obtained by adding place of articulation clues along with context information. Furthermore, diagnostic information provided by the proposed approach can be useful to the designers to further enhance the system performance.

We have recently proposed a universal acoustic characterisation to foreign accent recognition, in which any spoken foreign accent was described in terms of a common set of fundamental speech attributes. Although experimental evidence demonstrated the feasibility of our approach, we belive that speech attributes, namely manner and place of articulation, can be better modelled by a deep neural network. In this work, we propose the use of deep neural network trained on telephone bandwidth material from different languages to improve the proposed universal acoustic characterisation. We demonstrate that deeper neural architectures enhance the attribute classification accuracy. Furthermore, we show that improvements in attribute classification carry over to foreign accent recognition by producing a 21% relative improvement over previous baseline on spoken Finnish, and a 5.8% relative improvement on spoken English

We propose a hybrid approach to foreign accent recognition combining both phonotactic and spectral based systems by treating the problem as a spoken language recognition task. We extract speech attribute features that represent speech and acoustic cues reflecting foreign accents of a speaker to obtain feature streams that are modeled with the i-vector methodology. Testing on the Finnish Language Proficiency exam corpus, we find our proposed technique to achieve a significant performance improvement over the state-of-the-art systems using only spectral based features.

We adopt automatic language recognition methods to study dialect levelling - a phenomenon that leads to reduced structural differences among dialects in a given spoken language. In terms of dialect characterisation, levelling is a nuisance variable that adversely affects recognition accuracy: The more similar two dialects are, the harder it is to set them apart. We address levelling in Finnish regional dialects using a new SAPU (Satakunta in Speech) corpus containing material from Satakunta (South-Western Finland) between 2007 and 2013. To define a compact and universal set of sound units to characterize dialects, we adopt speech attributes features, namely manner and place of articulation. It will be shown that speech attribute distributions can indeed characterise differences among dialects. Experiments with an i-vector system suggest that (1) the attribute features achieve higher dialect recognition accuracy and (2) they are less sensitive against age-related levelling in comparison to traditional spectral approach.

I-vector based recognition is a well-established technique in state-of-the-art speaker and language recognition but its use in dialect and accent classification has received less attention. We represent an experimental study of i-vector based dialect classi-fication, with a special focus on foreign accent detection from spoken Finnish. Using the CallFriend corpus, we first study how recognition accuracy is affected by the choices of vari-ous i-vector system parameters, such as the number of Gaus-sians, i-vector dimensionality and reduction method. We then apply the same methods on the Finnish national foreign lan-guage certificate (FSD) corpus and compare the results to tra-ditional Gaussian mixture model- universal background model (GMM-UBM) recognizer. The results, in terms of equal error rate, indicate that i-vectors outperform GMM-UBM as one ex-pects. We also notice that in foreign accent detection, 7 out of 9 accents were more accurately detected by Gaussian scoring than by cosine scoring. Index Terms: Dialect recognition, foreign accent recognition, i-vector, GMM-UBM, Finnish language

Today, many social interactions and services depend on gender. In this paper, we introduce a single image gender classification algorithm using combination of appearance-based and geometric-based features. These include Discrete Cosine Transform (DCT), and Local Binary Pattern (LBP), and geometrical distance feature (GDF). The novel feature, GDF proposed in this paper, is inspired from physiological differences between male and female faces. Combination of appearance-based features (DCT and LBP) with geometric-based feature (GDF) leads to higher gender classification accuracy. Our system estimates gender of the input image based on the majority rule. If the results of DCT and LBP features are not identical, gender classification will be based on GDF feature. The proposed method was evaluated on two databases: AR and ethnic. Experimental results show that the novel geometric feature improves the gender classification accuracy by 13%.