Subcellular Protein Localisation in the Human Protein Atlas using Ensembles of Diverse Deep Architectures

Automated visual localisation of subcellular proteins can accelerate our understanding of cell function in health and disease. Despite recent advances in machine learning (ML), humans still attain superior accuracy by using diverse clues. We show how this gap can be narrowed by addressing three key aspects: (i) automated improvement of cell annotation quality, (ii) new Convolutional Neural Network (CNN) architectures supporting unbalanced and noisy data, and (iii) informed selection and fusion of multiple & diverse machine learning models. We introduce a new "AI-trains-AI" method for improving the quality of weak labels and propose novel CNN architectures exploiting wavelet filters and Weibull activations. We also explore key factors in the multi-CNN ensembling process by analysing correlations between image-level and cell-level predictions. Finally, in the context of the Human Protein Atlas, we demonstrate that our system achieves state-of-the-art performance in the multi-label single-cell classification of protein localisation patterns. It also significantly improves generalisation ability.

ACTNET: end-to-end learning of feature activations and multi-stream aggregation for effective instance image retrieval

We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio (SNR) of deep convolutional feature maps. Further, we introduce a controlled multi-stream aggregation, where complementary deep features from different convolutional layers are optimally transformed and balanced using our novel activation layers, before aggregation into a global descriptor. Importantly, the learnable parameters of our activation blocks are explicitly trained, together with the CNN parameters, in an end-to-end manner minimising triplet loss. This means that our network jointly learns the CNN filters and their optimal activation and aggregation for retrieval tasks. To our knowledge, this is the first time parametric functions have been used to control and learn optimal aggregation. We conduct an in-depth experimental study on three non-linear activation functions: Sine-Hyperbolic, Exponential and modified Weibull, showing that while all bring significant gains the Weibull function performs best thanks to its ability to equalise strong activations. The results clearly demonstrate that our ACTNET architecture significantly enhances the discriminative power of deep features, improving significantly over the state-of-the-art retrieval results on all datasets.

ACTNET: end-to-end learning of feature activations and aggregation for effective instance image retrieval

We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio (SNR) of deep convolutional feature maps. This works in tandem with multi-stream aggregation, where complementary deep features from different convolutional layers are transformed and balanced, using our novel activation layer, before aggregation into a global descriptor. Importantly, the learnable parameters of activation blocks are explicitly trained, jointly with the CNN parameters, in an end-to-end manner minimising triplet loss. This means that our network jointly learns the CNN filters and their optimal aggregation for the retrieval task. To our knowledge, this is the first time parametric functions are used to control and learn optimal aggregation. We conduct an in-depth experimental study on three non-linear activation functions: Sine-Hyperbolic, Exponential and modified Weibull, showing that while all bring significant gains the Weibull function performs best thanks to its ability to equalise strong activations. The results clearly demonstrate that activation functions significantly enhance the discriminative power of deep features, leading to state-of-the-art retrieval results.

REMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval

This paper addresses the problem of very large-scale image retrieval, focusing on improving its accuracy and robustness. We target enhanced robustness of search to factors such as variations in illumination, object appearance and scale, partial occlusions, and cluttered backgrounds - particularly important when search is performed across very large datasets with significant variability. We propose a novel CNN-based global descriptor, called REMAP, which learns and aggregates a hierarchy of deep features from multiple CNN layers, and is trained end-to-end with a triplet loss. REMAP explicitly learns discriminative features which are mutually-supportive and complementary at various semantic levels of visual abstraction. These dense local features are max-pooled spatially at each layer, within multi-scale overlapping regions, before aggregation into a single image-level descriptor. To identify the semantically useful regions and layers for retrieval, we propose to measure the information gain of each region and layer using KL-divergence. Our system effectively learns during training how useful various regions and layers are and weights them accordingly. We show that such relative entropy-guided aggregation outperforms classical CNN-based aggregation controlled by SGD. The entire framework is trained in an end-to-end fashion, outperforming the latest state-of-the-art results. On image retrieval datasets Holidays, Oxford and MPEG, the REMAP descriptor achieves mAP of 95.5%, 91.5%, and 80.1% respectively, outperforming any results published to date. REMAP also formed the core of the winning submission to the Google Landmark Retrieval Challenge on Kaggle.