Ophthalmology

Not Blind but Silenced: Rebalancing Vision and Language via Adversarial Counter-Commonsense Equilibrium

During MLLM decoding, attention often abnormally concentrates on irrelevant image tokens. While existing research dismisses this as invalid noise and forcibly redirects attention to compel focusing on key image information, we argue these tokens are critical carriers of visual and narrative logic, and such coercive corrections exacerbate visual-language imbalance. Adopting a "decoding-as-game" per...

Beyond the Last Layer: Multi-Layer Representation Fusion for Visual Tokenizatio

Representation autoencoders that reuse frozen pretrained vision encoders as visual tokenizers have achieved strong reconstruction and generation quality. However, existing methods universally extract features from only the last encoder layer, discarding the rich hierarchical information distributed across intermediate layers. We show that low-level visual details survive in the last layer merely a...

The German National Cohort: Ophthalmological Assessment, Baseline Profile and Potential for AI-based Eye Research

Objective: To describe the ophthalmic examination protocol within the German National Cohort (NAKO) / NAKO Gesundheitsstudie, to report the baseline p...

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are lim...

LensVLM: Selective Context Expansion for Compressed Visual Representation of Text

Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into lo...

An extremely coarse feedback signal is sufficient for learning human-aligned visual representations

Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that ha...

Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization

As a key technique in multi-modal processing, infrared and visible image fusion (IVIF) plays a crucial role in integrating complementary spectral info...

Metonymy in vision models undermines attention-based interpretability

Part-based reasoning is a classical strategy to make a computer vision model directly focus on the object parts that are relevant to the downstream ta...

Retina-RAG: Retrieval-Augmented Vision-Language Modeling for Joint Retinal Diagnosis and Clinical Report Generation

Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems are lim...

Conserved neuroectodermal aging encodes primate health and longevity

Neuroectoderm-derived tissues are highly metabolically active and exhibit minimal regenerative turnover, rendering them uniquely vulnerable to age-rel...

Spatial remodeling of the urothelial carcinoma tumor microenvironment shapes response to neoadjuvant atezolizumab

The ABACUS study was a single arm, phase II trial evaluating neoadjuvant atezolizumab in operable urothelial carcinoma. Initial bulk transcriptomic an...

Modifying integrated nursery management through the lens of mycorrhizal ecology improves radiata pine seedling performance and reshapes root mycobiome structure at operational industry scale

Early management decisions in operational forestry are critical for plantation success because it strongly influences seedling quality at planting. Be...

Classification of Smartphone Interaction Using Multimodal Physiological Signals with a Brain-Body Spatio-Temporal Transformer

Distinct smartphone interaction behaviors, like short-form video scrolling and mobile gaming, elicit qualitatively different cognitive and physiologic...

Automated behavioral segmentation and markerless pose tracking of mice during spaceflight

The NASA Rodent Habitat aboard the International Space Station enabled long-duration studies of behavioral responses to spaceflight, but video-based b...

Text-Conditional JEPA for Learning Semantically Rich Visual Representations

Image-based Joint-Embedding Predictive Architecture (I-JEPA) offers a promising approach to visual self-supervised learning through masked feature pre...

CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis

High-throughput plant phenotyping, the quantitative measurement of observable plant traits, is critical for modern breeding but remains constrained by...

Large Language Models are Universal Reasoners for Visual Generation

Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM b...

Representation learning from OCT images

Optical Coherence Tomography (OCT) has become one of the most used imaging modality in ophthalmology. It provides high-resolution, non-invasive visual...

OphMAE: Bridging Volumetric and Planar Imaging with a Foundation Model for Adaptive Ophthalmological Diagnosis

The advent of foundation models has heralded a new era in medical artificial intelligence (AI), enabling the extraction of generalizable representatio...

PubMed-Ophtha: An open resource for training ophthalmology vision-language models on scientific literature

Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets tha...