Latent Diffusion Model for DNA Sequence Generation

The harnessing of machine learning, especially deep generative models, has opened up promising avenues in the field of synthetic DNA sequence generation. Whilst Generative Adversarial Networks (GANs) have gained traction for this application, they often face issues such as limited sample diversity and mode collapse. On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation. In light of this, we propose a novel latent diffusion model, DiscDiff, tailored for discrete DNA sequence generation. By simply embedding discrete DNA sequences into a continuous latent space using an autoencoder, we are able to leverage the powerful generative abilities of continuous diffusion models for the generation of discrete data. Additionally, we introduce Fr\'echet Reconstruction Distance (FReD) as a new metric to measure the sample quality of DNA sequence generations. Our DiscDiff model demonstrates an ability to generate synthetic DNA sequences that align closely with real DNA in terms of Motif Distribution, Latent Embedding Distribution (FReD), and Chromatin Profiles. Additionally, we contribute a comprehensive cross-species dataset of 150K unique promoter-gene sequences from 15 species, enriching resources for future generative modelling in genomics. We will make our code public upon publication

MASK FACE INPAINTING BASED ON IMPROVED GENERATIVE ADVERSARIAL NETWORK

Face recognition technology has been widely used in all aspects of people's lives. However, the accuracy of face recognition is greatly reduced due to the obscuring of objects, such as masks and sunglasses. Wearing masks in public has been a crucial approach to preventing illness, especially since the Covid-19 outbreak. This poses challenges to applications such as face recognition. Therefore, the removal of masks via image inpainting has become a hot topic in the field of computer vision. Deep learning-based image inpainting techniques have taken observable results, but the restored images still have problems such as blurring and inconsistency. To address such problems, this paper proposes an improved inpainting model based on generative adversarial network: the model adds attention mechanisms to the sampling module based on pix2pix network; the residual module is improved by adding convolutional branches. The improved inpainting model can not only effectively restore faces obscured by face masks, but also realize the inpainting of randomly obscured images of human faces. To further validate the generality of the inpainting model, tests are conducted on the datasets of CelebA, Paris Street and Place2, and the experimental results show that both SSIM and PSNR have improved significantly

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior can enable fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code is freely available at http://github.com/adalca/neuron.Comment: Presented at CVPR 2018. IEEE CVPR proceedings pp. 9290-929

Semantic segmentation based on Deep learning for the detection of Cyanobacterial Harmful Algal Blooms (CyanoHABs) using synthetic images

Cyanobacterial Harmful Algal Blooms (CyanoHABs) in lakes and reservoirs have increased substantially in recent decades due to different environmental factors. Its early detection is a crucial issue to minimize health effects, particularly in potential drinking and recreational water bodies. The use of Autonomous Surface Vehicles (ASVs) equipped with machine vision systems (cameras) onboard, represents a useful alternative at this time. In this regard, we propose an image Semantic Segmentation approach based on Deep Learning with Convolutional Neural Networks (CNNs) for the early detection of CyanoHABs considering an ASV perspective. The use of these models is justified by the fact that with their convolutional architecture, it is possible to capture both, spectral and textural information considering the context of a pixel and its neighbors. To train these models it is necessary to have data, but the acquisition of real images is a difficult task, due to the capricious appearance of the algae on water surfaces sporadically and intermittently over time and after long periods of time, requiring even years and the permanent installation of the image capture system. This justifies the generation of synthetic data so that sufficiently trained models are required to detect CyanoHABs patches when they emerge on the water surface. The data generation for training and the use of the semantic segmentation models to capture contextual information determine the need for the proposal, as well as its novelty and contribution. Three datasets of images containing CyanoHABs patches are generated: (a) the first contains real patches of CyanoHABs as foreground and images of lakes and reservoirs as background, but with a limited number of examples; (b) the second, contains synthetic patches of CyanoHABs generated with state-of-the-art Style-based Generative Adversarial Network Adaptive Discriminator Augmentation (StyleGAN2-ADA) and Neural Style Transfer as foreground and images of lakes and reservoirs as background, and (c) the third set, is the combination of the previous two. Four model architectures for semantic segmentation (UNet++, FPN, PSPNet, and DeepLabV3+), with two encoders as backbone (ResNet50 and EfficientNet-b6), are evaluated from each dataset on real test images and different distributions. The results show the feasibility of the approach and that the UNet++ model with EfficientNet-b6, trained on the third dataset, achieves good generalization and performance for the real test images.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEComunidad Autónoma de MadridSpanish Ministry of Science, Innovation and UniversitiesMinistry of Education of PeruSpanish Ministry of Universitiespu

Bridging generative models and Convolutional Neural Networks for domain-agnostic segmentation of brain MRI

Segmentation of brain MRI scans is paramount in neuroimaging, as it is a prerequisite for many subsequent analyses. Although manual segmentation is considered the gold standard, it suffers from severe reproducibility issues, and is extremely tedious, which limits its application to large datasets. Therefore, there is a clear need for automated tools that enable fast and accurate segmentation of brain MRI scans. Recent methods rely on convolutional neural networks (CNNs). While CNNs obtain accurate results on their training domain, they are highly sensitive to changes in resolution and MRI contrast. Although data augmentation and domain adaptation techniques can increase the generalisability of CNNs, these methods still need to be retrained for every new domain, which requires costly labelling of images. Here, we present a learning strategy to make CNNs agnostic to MRI contrast, resolution, and numerous artefacts. Specifically, we train a network with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation approach where all generation parameters are drawn for each example from uniform priors. As a result, the network is forced to learn domain-agnostic features, and can segment real test scans without retraining. The proposed method almost achieves the accuracy of supervised CNNs on their training domain, and substantially outperforms state-of-the-art domain adaptation methods. Finally, based on this learning strategy, we present a segmentation suite for robust analysis of heterogeneous clinical scans. Overall, our approach unlocks the development of morphometry on millions of clinical scans, which ultimately has the potential to improve the diagnosis and characterisation of neurological disorders