Dmitry Minskiy

Unravelling protein distributions within individual cells is vital to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features… Show more

Unravelling protein distributions within individual cells is vital to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features correlation-based ensembling of novel architectures that boosts performance and aids generalisation. Large-scale data annotation is made feasible by our AI-trains-AI approach, which determines the visual integrity of cells and emphasises reliable labels for efficient training. In the Human Protein Atlas context, we demonstrate that HCPL is best performing in the single-cell classification of protein localisation patterns. To better understand the inner workings of HCPL and assess its biological relevance, we analyse the contributions of each system component and dissect the emergent features from which the localisation predictions are derived. Show less

This work explores how using mathematically defined filters in conjunction with deep CNN architectures could help address the fundamental challenges of deep learning. Networks that employ hand-crafted filters in combination with learnt representations are known as hybrid networks. Broadly, this work addresses the challenge of scaling the application scope of hybrid approaches from a few bespoke cases in data-limited scenarios to a broad range of applications and ensuring hybrids' performance… Show more

This work explores how using mathematically defined filters in conjunction with deep CNN architectures could help address the fundamental challenges of deep learning. Networks that employ hand-crafted filters in combination with learnt representations are known as hybrid networks. Broadly, this work addresses the challenge of scaling the application scope of hybrid approaches from a few bespoke cases in data-limited scenarios to a broad range of applications and ensuring hybrids' performance advantage regardless of the training data available.

We show that with the novel techniques proposed, it is possible to build large-scale practical hybrid architectures with superior performance. Hence, we debunk the commonly held view that such networks are only useful in niche environments and solely in data-limited application scenarios. Thus, we uncover an exciting future for hybrid networks and motivate further research and development in this area. Show less

Recent work showed that hybrid networks, which combine predefined and learnt filters within a single architecture, are more amenable to theoretical analysis and less prone to overfitting in data-limited scenarios. However, their performance has yet to prove competitive against the conventional counterparts when sufficient amounts of training data are available. In an attempt to address this core limitation of current hybrid networks, we introduce an Efficient Hybrid Network (E-HybridNet). We… Show more

Recent work showed that hybrid networks, which combine predefined and learnt filters within a single architecture, are more amenable to theoretical analysis and less prone to overfitting in data-limited scenarios. However, their performance has yet to prove competitive against the conventional counterparts when sufficient amounts of training data are available. In an attempt to address this core limitation of current hybrid networks, we introduce an Efficient Hybrid Network (E-HybridNet). We show that it is the first scattering based approach that consistently outperforms its conventional counterparts on a diverse range of datasets. It is achieved with a novel inductive architecture that embeds scattering features into the network flow using Hybrid Fusion Blocks. We also demonstrate that the proposed design inherits the key property of prior hybrid networks - an effective generalisation in data-limited scenarios. Our approach successfully combines the best of the two worlds: flexibility and power of learnt features and stability and predictability of scattering representations. Show less

Hybrid networks combine fixed and learnable filters to address the limitations of fully trained CNNs such as poor interpretability, high computational complexity and a need for large training sets. Many hybrid designs were proposed, utilising different filter types, backbone CNNs and different approaches to learning. They were evaluated on different (and often simplistic) datasets, making it difficult to understand their relative performance, their strengths and weaknesses, also there are no… Show more

Hybrid networks combine fixed and learnable filters to address the limitations of fully trained CNNs such as poor interpretability, high computational complexity and a need for large training sets. Many hybrid designs were proposed, utilising different filter types, backbone CNNs and different approaches to learning. They were evaluated on different (and often simplistic) datasets, making it difficult to understand their relative performance, their strengths and weaknesses, also there are no design guides on building a hybrid application for the problem at hand. We present and benchmark a collection of 27 networks, some new learnable extensions to existing designs, all within a framework that allows an assessment of a wide range of scattering types and their effects on the system performance. Also, we outline application scenarios most suitable for hybrid networks, identify previously unnoticed trends and provide guidance in building hybrids. Show less