arXiv:2601. 12282v2 Announce Type: replace-cross Abstract: The functions of different regions of the human brain are closely linked to their distinct cytoarchitecture, which is defined by the spatial arrangement and morphology of the cells.
arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...
arXiv:2606. 04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience.
PixVL introduces a self‑supervised post‑training framework for pixel‑level multimodal large language models, addressing the scarcity of mask‑text pairs and optimization interference between region segmentation and region understanding. It employs a unified Mask‑Text Consistency Cycle that generates and self‑verifies regional descriptions using confuser‑aware semantic verification and cross‑view verification, while a quality‑coupled bidirectional learning strategy turns segmentation and understanding into mutual generators and verifiers. Experiments show that PixVL improves performance on both region understanding and segmentation tasks.
arXiv:2606. 00275v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive performance on multimodal tasks through scaled architectures and extensive training.
arXiv:2608. 10657v1 Announce Type: cross Abstract: Leukemia cell image classification is challenged by real-world domain shifts from acquisition, staining, illumination, and site protocols, causing single-dataset models to generalize poorly in real clinical scenarios.
The paper introduces MedREAL, a unified framework that aligns linguistic reasoning with spatial grounding for medical visual question answering and segmentation. MedREAL employs Seg Anchored Reasoning Pooling (SARP) to extract semantic evidence from segmentation tokens and a Reasoning-to-Visual (R2V) fusion mechanism to integrate these features into a segmentation pipeline. Using the newly created MedRAVS-13K dataset, MedREAL achieves superior performance, reporting 68.49% gIoU and 70.47% cIoU, and generates evidence masks that consistently match textual diagnoses.
The paper introduces TEAMS, a vision‑language Mamba snake framework that enhances deep snake instance segmentation. It adds a Spatiotemporal Snake Evolution Strategy to handle complex shapes, a Contour Morphology‑Aware Mamba to improve fine‑grained detail capture, and a Text‑prompted Collaborative Dual‑Head Snake to integrate textual cues and reduce detection errors. Experiments on five medical imaging datasets show TEAMS surpasses existing methods, achieving significant gains in mDice and mBF metrics.
arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
arXiv:2609.32352v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have shown increasing potential for medical image understanding, yet their capabilities in ophthalmic imaging r...
The paper introduces PureVision, a geometry‑supervised framework for medical vision‑language models that tackles the challenge of interpreting multi‑phenotype lesions. It comprises PureEyes, which imposes geometric constraints to preserve anatomical hierarchies and phenotypic subclass relationships, and PureNeurons, which aggregates lesion‑specific anatomical and phenotypic evidence in a learned latent space. Experiments on LIDC‑IDRI, CBIS‑DDSM, and 3DReasonKnee show that PureVision enhances lesion grounding and phenotype characterization in visual question answering and radiology report generation.
Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation. However, these models still struggle to interpret multi-phenotype lesions whose diagnosis requi...