Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset and invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. TopCoW dataset was the first public dataset with voxel-level annotations for CoW's 13 vessel components, made possible by virtual-reality (VR) technology. It was also the first dataset with paired MRA and CTA from the same patients. TopCoW challenge aimed to tackle the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant's topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation

We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality, despite the wide range of available cerebrovascular imaging techniques. This can lead to significant distribution shifts that negatively impact the generalization across modalities. By relying on annotated angiographies and a limited number of annotated venographies, our framework accomplishes image-to-image translation and semantic segmentation, leveraging a disentangled and semantically rich latent space to represent heterogeneous data and perform image-level adaptation from source to target domains. Moreover, we reduce the typical complexity of cycle-based architectures and minimize the use of adversarial training, which allows us to build an efficient and intuitive model with stable training. We evaluate our method on magnetic resonance angiographies and venographies. While achieving state-of-the-art performance in the source domain, our method attains a Dice score coefficient in the target domain that is only 8.9% lower, highlighting its promising potential for robust cerebrovascular image segmentation across different modalities.

Binary domain generalization for sparsifying binary neural networks

Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices. Despite their success, BNNs still suffer from a fixed and limited compression factor that may be explained by the fact that existing pruning methods for full-precision DNNs cannot be directly applied to BNNs. In fact, weight pruning of BNNs leads to performance degradation, which suggests that the standard binarization domain of BNNs is not well adapted for the task. This work proposes a novel more general binary domain that extends the standard binary one that is more robust to pruning techniques, thus guaranteeing improved compression and avoiding severe performance losses. We demonstrate a closed-form solution for quantizing the weights of a full-precision network into the proposed binary domain. Finally, we show the flexibility of our method, which can be combined with other pruning strategies. Experiments over CIFAR-10 and CIFAR-100 demonstrate that the novel approach is able to generate efficient sparse networks with reduced memory usage and run-time latency, while maintaining performance.

JoB-VS: Joint Brain-Vessel Segmentation in TOF-MRA Images

We propose the first joint-task learning framework for brain and vessel segmentation (JoB-VS) from Time-of-Flight Magnetic Resonance images. Unlike state-of-the-art vessel segmentation methods, our approach avoids the pre-processing step of implementing a model to extract the brain from the volumetric input data. Skipping this additional step makes our method an end-to-end vessel segmentation framework. JoB-VS uses a lattice architecture that favors the segmentation of structures of different scales (e.g., the brain and vessels). Its segmentation head allows the simultaneous prediction of the brain and vessel mask. Moreover, we generate data augmentation with adversarial examples, which our results demonstrate to enhance the performance. JoB-VS achieves 70.03% mean AP and 69.09% F1-score in the OASIS-3 dataset and is capable of generalizing the segmentation in the IXI dataset. These results show the adequacy of JoB-VS for the challenging task of vessel segmentation in complete TOF-MRA images.

Fairness and bias correction in machine learning for depression prediction: results from four different study populations

A significant level of stigma and inequality exists in mental healthcare, especially in under-served populations, which spreads through collected data. When not properly accounted for, machine learning (ML) models learned from data can reinforce the structural biases already present in society. Here, we present a systematic study of bias in ML models designed to predict depression in four different case studies covering different countries and populations. We find that standard ML approaches show regularly biased behaviors. However, we show that standard mitigation techniques, and our own post-hoc method, can be effective in reducing the level of unfair bias. We provide practical recommendations to develop ML models for depression risk prediction with increased fairness and trust in the real world. No single best ML model for depression prediction provides equality of outcomes. This emphasizes the importance of analyzing fairness during model selection and transparent reporting about the impact of debiasing interventions.

Sparsifying Binary Networks

Binary neural networks (BNNs) have demonstrated their ability to solve complex tasks with comparable accuracy as full-precision deep neural networks (DNNs), while also reducing computational power and storage requirements and increasing the processing speed. These properties make them an attractive alternative for the development and deployment of DNN-based applications in Internet-of-Things (IoT) devices. Despite the recent improvements, they suffer from a fixed and limited compression factor that may result insufficient for certain devices with very limited resources. In this work, we propose sparse binary neural networks (SBNNs), a novel model and training scheme which introduces sparsity in BNNs and a new quantization function for binarizing the network's weights. The proposed SBNN is able to achieve high compression factors and it reduces the number of operations and parameters at inference time. We also provide tools to assist the SBNN design, while respecting hardware resource constraints. We study the generalization properties of our method for different compression factors through a set of experiments on linear and convolutional networks on three datasets. Our experiments confirm that SBNNs can achieve high compression rates, without compromising generalization, while further reducing the operations of BNNs, making SBNNs a viable option for deploying DNNs in cheap, low-cost, limited-resources IoT devices and sensors.

Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?

Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to compare only learning-based methods in their articles, abandoning some more conventional approaches. As a result, the community in this field has been encouraged to propose increasingly complex learning-based models mainly based on deep neural networks. To our knowledge, there are no comparative studies between conventional, machine learning-based and, deep neural network methods for the detection of anomalies in multivariate time series. In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets. By analyzing and comparing the performance of each of the sixteen methods, we show that no family of methods outperforms the others. Therefore, we encourage the community to reincorporate the three categories of methods in the anomaly detection in multivariate time series benchmarks.

AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics

While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices. In this work, we present an AI-based Brain-Computer Interface (BCI) in which humans and machines interact to express feelings artistically. This system and its production of images give opportunities to reflect on the complexities and range of human emotions and their expressions. In this discussion, we seek to understand the dynamics of this interaction to reach better co-existence in fairness, inclusion, and aesthetics.

Exploring auditory acoustic features for the diagnosis of the Covid-19

The current outbreak of a coronavirus, has quickly escalated to become a serious global problem that has now been declared a Public Health Emergency of International Concern by the World Health Organization. Infectious diseases know no borders, so when it comes to controlling outbreaks, timing is absolutely essential. It is so important to detect threats as early as possible, before they spread. After a first successful DiCOVA challenge, the organisers released second DiCOVA challenge with the aim of diagnosing COVID-19 through the use of breath, cough and speech audio samples. This work presents the details of the automatic system for COVID-19 detection using breath, cough and speech recordings. We developed different front-end auditory acoustic features along with a bidirectional Long Short-Term Memory (bi-LSTM) as classifier. The results are promising and have demonstrated the high complementary behaviour among the auditory acoustic features in the Breathing, Cough and Speech tracks giving an AUC of 86.60% on the test set.

Optimization Strategies in Multi-Task Learning: Averaged or Independent Losses?

In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective functions. Although the computational advantages of this strategy are clear, the complexity of the resulting loss landscape has not been studied in the literature. Arguably, its optimization may be more difficult than a separate optimization of the constituting task-specific objectives. In this work, we investigate the benefits of such an alternative, by alternating independent gradient descent steps on the different task-specific objective functions and we formulate a novel way to combine this approach with state-of-the-art optimizers. As the separation of task-specific objectives comes at the cost of increased computational time, we propose a random task grouping as a trade-off between better optimization and computational efficiency. Experimental results over three well-known visual MTL datasets show better overall absolute performance on losses and standard metrics compared to an averaged objective function and other state-of-the-art MTL methods. In particular, our method shows the most benefits when dealing with tasks of different nature and it enables a wider exploration of the shared parameter space. We also show that our random grouping strategy allows to trade-off between these benefits and computational efficiency.