Self-supervised pseudo-colorizing of masked cells

Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, we introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. We use a physics-informed pseudo-spectral colormap that is well suited for colorizing cell topology. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection. Inspired by the recent success of masked image modeling, we additionally mask out cell parts and train to reconstruct these parts to further enrich the learned representations. We compare our pre-training method with self-supervised frameworks including contrastive learning (SimCLR), masked autoencoders (MAEs), and edge-based self-supervision. We build upon our previous work and train hybrid models for cell detection, which contain both convolutional and vision transformer modules. Our pre-training method can outperform SimCLR, MAE-like masked image modeling, and edge-based self-supervision when pre-training on a diverse set of six fluorescence microscopy datasets. Code is available at: https://github.com/roydenwa/pseudo-colorize-masked-cell

Self-supervised pseudo-colorizing of masked cells

Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, we introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. We use a physics-informed pseudo-spectral colormap that is well suited for colorizing cell topology. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection. Inspired by the recent success of masked image modeling, we additionally mask out cell parts and train to reconstruct these parts to further enrich the learned representations. We compare our pre-training method with self-supervised frameworks including contrastive learning (SimCLR), masked autoencoders (MAEs), and edge-based self-supervision. We build upon our previous work and train hybrid models for cell detection, which contain both convolutional and vision transformer modules. Our pre-training method can outperform SimCLR, MAE-like masked image modeling, and edge-based self-supervision when pre-training on a diverse set of six fluorescence microscopy datasets. Code is available at: https://github.com/roydenwa/pseudo-colorize-masked-cells

Fractional super-resolution of voxelized point clouds

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2021.Neste trabalho, apresentamos um método para super-resolver nuvens de pontos por um fator fracionário, utilizando um dicionário construído a partir de auto-similaridades presentes na versão subamostrada. Dada a geometria de uma nuvem de pontos subamostrada , juntamente com o correspondente fator de subamostragem , 1 < ≤ 2, o método proposto determina o conjunto de pontos que podem ter gerado e estima quais desses pontos, de fato, existem em (super resolução). Considerando que a geometria de uma nuvem de pontos é aproximadamente auto similar em diferentes escalas de subamostragem, cria-se um dicionário relacionando a configuração de ocupação da vizinhança com a ocupação de nós-filhos. O dicionário é obtido a partir de nova subamostragem da geometria de entrada utilizando o mesmo fator . Desta forma, leva-se em conta as irregularidades da subamostragem por fatores fracionários no desenvolvimento da super-resolução. A textura da nuvem de pontos é interpolada utilizando a média ponderada das cores de vizinhos adjacentes. Diversos conteúdos de diferentes fontes foram testados e resultados interessantes foram obtidos. Adicionalmente, apresentamos uma aplicação direta do método de super-resolução para melhorar a compressão de nuvens de pontos utilizando o codificador G-PCC do MPEG.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).We present a method to super-resolve voxelized point clouds downsampled by a fractional factor, using a lookup-table (LUT) constructed from self-similarities from their own downsampled neighbourhoods. Given a downsampled point cloud geometry , and its corresponding fractional downsampling factor , 1 < ≤ 2, the proposed method determines the set of positions that may have generated , and estimates which of these positions were indeed occupied (super resolution). Assuming that the geometry of a point cloud is approximately self-similar at different scales, a LUT relating downsampled neighbourhood configurations with children occupancy configurations can be estimated by further downsampling the input point cloud, and by taking into account the irregular children distribution derived from fractional downsampling. For completeness, we also interpolate texture by averaging colors from adjacent neighbour voxels. Extensive tests over different datasets are presented, and interesting results were obtained. We further present a direct application to improve point cloud compression using MPEG’s G-PCC codec

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