ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis

Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial Networks (GANs) have provided innovative approaches for histopathological image analysis, they suffer from limitations such as mode collapse and overfitting in discriminator. Recently, Denoising Diffusion models have demonstrated promising results in computer vision. These models exhibit superior stability during training, better distribution coverage, and produce high-quality diverse images. Additionally, they display a high degree of resilience to noise and perturbations, making them well-suited for use in digital pathology, where images commonly contain artifacts and exhibit significant variations in staining. In this paper, we present a novel approach, namely ViT-DAE, which integrates vision transformers (ViT) and diffusion autoencoders for high-quality histopathology image synthesis. This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images. We demonstrate the effectiveness of ViT-DAE on three publicly available datasets. Our approach outperforms recent GAN-based and vanilla DAE methods in generating realistic images.Comment: Submitted to MICCAI 202

fMRI in patients with lumbar disc disease: a paradigm to study patients over time

Low back pain is a common human ailment. It is estimated that over 70% of the population will experience low back pain that will require medication and/or medical attention. There are many causes for low back pain, one being herniation of the discs of the lumbar spine. Treatment options are very limited. Why patients develop chronic pain especially when there is no known organic cause or when the offending painful stimulus has been removed remains poorly understood. Functional magnetic resonance imaging (fMRI) is a technique that allows researchers to image which regions of the brain that are activated during motor, cognitive, and sensory experiences. Using fMRI to study pain has revealed new information about how the brain responds to painful stimuli and what regions of the brain are activated during pain. However, many of the paradigms used do not replicate the subject’s pain or use painful stimuli in volunteers without pain. Also, following patients from their acute phase of pain to the chronic phase with serial fMRI has not been performed. In this study we developed a paradigm that would allow studying patients with low back pain and leg pain including lumbar radiculopathy to better mimic a clinical pain syndrome and to have a method of following patients with this type of pain over time

Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.Comment: To be published in proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 202

Halcyon -- A Pathology Imaging and Feature analysis and Management System

Halcyon is a new pathology imaging analysis and feature management system based on W3C linked-data open standards and is designed to scale to support the needs for the voluminous production of features from deep-learning feature pipelines. Halcyon can support multiple users with a web-based UX with access to all user data over a standards-based web API allowing for integration with other processes and software systems. Identity management and data security is also provided.Comment: 15 pages, 11 figures. arXiv admin note: text overlap with arXiv:2005.0646

Open and reusable deep learning for pathology with WSInfer and QuPath

The field of digital pathology has seen a proliferation of deep learning models in recent years. Despite substantial progress, it remains rare for other researchers and pathologists to be able to access models published in the literature and apply them to their own images. This is due to difficulties in both sharing and running models. To address these concerns, we introduce WSInfer: a new, open-source software ecosystem designed to make deep learning for pathology more streamlined and accessible. WSInfer comprises three main elements: 1) a Python package and command line tool to efficiently apply patch-based deep learning inference to whole slide images; 2) a QuPath extension that provides an alternative inference engine through user-friendly and interactive software, and 3) a model zoo, which enables pathology models and metadata to be easily shared in a standardized form. Together, these contributions aim to encourage wider reuse, exploration, and interrogation of deep learning models for research purposes, by putting them into the hands of pathologists and eliminating a need for coding experience when accessed through QuPath. The WSInfer source code is hosted on GitHub and documentation is available at https://wsinfer.readthedocs.io