Latest AI and machine learning research in ophthalmology for healthcare professionals.
Transformers have become foundational for visual tasks such as object detection, semantic segmentation, and video understanding, but their quadratic complexity in attention mechanisms presents scalability challenges. To address these limitations, the Mamba architecture utilizes state-space models (SSMs) for linear scalability, efficient processing, and improved contextual awareness. This paper i...
The choroid, a highly vascular layer behind the retina, is an extension of the central nervous system and has parallels with the renal cortex, with blood flow far exceeding that of the brain and kidney. Thus, there has been growing interest of choroidal blood flow reflecting physiological status of systemic disease. Optical coherence tomography (OCT) enables high-resolution imaging of the choroi...
Manipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects...
We identify two major limitations in the existing studies on retinal vessel segmentation: (1) Most existing works are restricted to one modality, i....
Visual reasoning -- the ability to interpret the visual world -- is crucial for embodied agents that operate within three-dimensional scenes. Progre...
To truly understand vision models, we must not only interpret their learned features but also validate these interpretations through controlled expe...
Retinal prostheses restore vision by electrically stimulating surviving neurons, but calibrating perceptual thresholds (i.e., the minimum stimulus i...
Autofocus is necessary for high-throughput and real-time scanning in microscopic imaging. Traditional methods rely on complex hardware or iterative ...
Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical ...
The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1...
Recent vision foundation models (VFMs), typically based on Vision Transformer (ViT), have significantly advanced numerous computer vision tasks. Des...
While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory...
By monitoring temporal contrast, event-based vision sensors can provide high temporal resolution and low latency while maintaining low power consump...
Recent advances in large-scale machine learning have produced high-capacity foundation models capable of adapting to a broad array of downstream tas...
The rapid advancements in large language models (LLMs) have unlocked their potential for multimodal tasks, where text and visual data are processed ...
Generative Agent-Based Modeling (GABM) is an emerging simulation paradigm that combines the reasoning abilities of Large Language Models with tradit...
Vision Large Language Models (VLLMs) integrate visual data processing, expanding their real-world applications, but also increasing the risk of gene...
This study examines the capabilities of the Vision Transformer (ViT) model in generating visual embeddings for images of auto parts sourced from onl...
This study introduces SEFI (SEgmentation-Free Integration), a novel method for integrating morphological features of cell nuclei with spatial transc...
Large Language Models primarily operate through text-based inputs and outputs, yet human emotion is communicated through both verbal and non-verbal ...