Self-supervised learning (SSL) has emerged as a powerful approach for learning biologically meaningful representations of single-cell data. To establish best practices in this domain, we present a comprehensive benchmark evaluating eight SSL methods across three downstream tasks and eight datasets, with various data augmentation strategies. Our results demonstrate that SimCLR and VICReg consistently outperform other methods across different tasks. Furthermore, we identify random masking as the most effective augmentation technique. This benchmark provides valuable insights into the application of SSL to single-cell data analysis, bridging the gap between SSL and single-cell biology.

Philip Toma^*, Olga Ovcharenko^*, Imant Daunhawer, Julia Vogt, Florian Barkmann^†, Valentina Boeva^†
* denotes shared first authorship, ^† denotes shared last authorship

Preprint

06.11.2024

DOICode

The structure of many real-world datasets is intrinsically hierarchical, making the modeling of such hierarchies a critical objective in both unsupervised and supervised machine learning. Recently, novel approaches for hierarchical clustering with deep architectures have been proposed. In this work, we take a critical perspective on this line of research and demonstrate that many approaches exhibit major limitations when applied to realistic datasets, partly due to their high computational complexity. In particular, we show that a lightweight procedure implemented on top of pre-trained non-hierarchical clustering models outperforms models designed specifically for hierarchical clustering. Our proposed approach is computationally efficient and applicable to any pre-trained clustering model that outputs logits, without requiring any fine-tuning. To highlight the generality of our findings, we illustrate how our method can also be applied in a supervised setup, recovering meaningful hierarchies from a pre-trained ImageNet classifier.

Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser, Imant Daunhawer^†, Julia E. Vogt^†
† denotes shared last authorship

Preprint

10.10.2024

DOI

Performant machine learning models are becoming increasingly complex and large. Due to their black-box design, they often have limited utility in exploratory data analysis and evoke little trust in non-expert users. Interpretable and explainable machine learning research emerges from application domains where, for technical or social reasons, interpreting or explaining the model's predictions or parameters is deemed necessary. In practice, interpretability and explainability are attained by (i) constructing models understandable to users by design and (ii) developing techniques to help explain already-trained black-box models. This thesis develops interpretable and explainable machine learning models and methods tailored to applications in biomedical and healthcare data analysis. The challenges posed by this domain require nontrivial solutions and deserve special treatment. In particular, we consider practical use cases with high-dimensional and unstructured data types, diverse application scenarios, and different stakeholder groups, which all dictate special design considerations. We demonstrate that, beyond social and ethical value, interpretability and explainability help in (i) performing exploratory data analysis, (ii) supporting medical professionals' decisions, (iii) facilitating interaction with users, and (iv) debugging the model. Our contributions are structured in two parts, tackling distinct research questions from the perspective of biomedical and healthcare applications. Firstly, we explore how to develop and incorporate inductive biases to render neural network models interpretable. Secondly, we study how to leverage explanation methods to interact with and edit already-trained black-box models. This work spans several model and method families, including interpretable neural network architectures, prototype- and concept-based models, and attribution methods. Our techniques are motivated by classic biomedical and healthcare problems, such as time series, survival, and medical image analysis. In addition to new model and method development, we concentrate on empirical comparison, providing proof-of-concept results on real-world biomedical benchmarks. Thus, the primary contribution of this thesis is the development of interpretable models and explanation methods with a principled treatment of specific biomedical and healthcare data types to solve application- and user-grounded problems. Through concrete use cases, we show that interpretability and explainability are context- and user-specific and, therefore, must be studied in conjunction with their application domain. We hope that our methodological and empirical contributions pave the way for future application- and user-driven interpretable and explainable machine learning research.

Ricards Marcinkevics

Doctoral thesis

24.09.2024

LinkDOI

Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each year globally. The heterogeneity among SCD victims, ranging from individuals with severe heart failure to seemingly healthy individuals, poses a significant challenge for effective risk assessment. Conventional risk stratification, which primarily relies on left ventricular ejection fraction, has resulted in only modest efficacy of implantable cardioverter-defibrillators for SCD prevention. In response, artificial intelligence (AI) holds promise for personalized SCD risk prediction and tailoring preventive strategies to the unique profiles of individual patients. Machine and deep learning algorithms have the capability to learn intricate nonlinear patterns between complex data and defined end points and leverage these to identify subtle indicators and predictors of SCD that may not be apparent through traditional statistical analysis. However, despite the potential of AI to improve SCD risk stratification, there are important limitations that need to be addressed. We aim to provide an overview of the current state-of-the-art of AI prediction models for SCD, highlight the opportunities for these models in clinical practice, and identify the key challenges hindering widespread adoption.

MZH Kolk, S Ruipérez-Campillo, AAM Wilde, RE Knops, SM Narayan, FVY Tjong

Heart Rhythm

06.09.2024

LinkDOI

Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correct wrongly predicted concept values to enhance the model's downstream performance. We propose Stochastic Concept Bottleneck Models (SCBMs), a novel approach that models concept dependencies. In SCBMs, a single-concept intervention affects all correlated concepts. Leveraging the parameterization, we derive an effective intervention strategy based on the confidence region. We show empirically on synthetic tabular and natural image datasets that our approach improves intervention effectiveness significantly. Notably, we showcase the versatility and usability of SCBMs by examining a setting with CLIP-inferred concepts, alleviating the need for manual concept annotations.

Moritz Vandenhirtz^*, Sonia Laguna^*, Ricards Marcinkevics, Julia E. Vogt
^* denotes shared first authorship

ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling, Workshop on Models of Human Feedback for AI Alignment, and Workshop on Humans, Algorithmic Decision-Making and Society

26.07.2024

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