Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

In this study we uncover the unexpected efficacy of residual-based large language models
(LLMs) as part of encoders for biomedical imaging tasks a domain traditionally devoid of …

The burgeoning integration of 3D medical imaging into healthcare has led to a substantial
increase in the workload of medical professionals. To assist clinicians in their diagnostic …

We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for
the semantic and instance segmentation of volumetric microscopy images, built upon …

Hair cells—the sensory cells of the vertebrate inner ear—bear at their apical surfaces a
bundle of actin-filled protrusions called stereocilia, which mediate the cells' …

Maps of the nervous system that identify individual cells along with their type, subcellular
components and connectivity have the potential to elucidate fundamental organizational …

The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses
to enable new insights into the workings of the brain. Reconstructing and analyzing the …

This paper presents a novel approach to enhance image-to-image generation by leveraging
the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We …

Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy
in the field of computer vision, harnessing the capabilities of machine learning (ML) to …