Automatic detection of cell-cycle stages using recurrent neural networks.

Mitosis is the process by which eukaryotic cells divide to produce two similar daughter cells with identical genetic material. Research into the process of mitosis is therefore of critical importance both for the basic understanding of cell biology and for the clinical approach to manifold pathologies resulting from its malfunctioning, including cancer. In this paper, we propose an approach to study mitotic progression automatically using deep learning.

Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.

Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations.

Advances in medical image analysis with vision Transformers: A comprehensive review.

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis.

Spectrum of intracranial complications of rhino-orbito-cerebral mucormycosis - resurgence in the era of COVID-19 pandemic: a pictorial essay.

Rhino-orbito-cerebral mucormycosis (ROCM) has regained significance following its resurgence in the second wave of the COVID-19 pandemic in India. Rapid and progressive intracranial spread occurs either by direct extension across the neural foraminae, cribriform plate/ethmoid, walls of sinuses, or angioinvasion. Having known to have a high mortality rate, especially with intracranial extension of disease, it becomes imperative to familiarise oneself with its imaging features.