arXiv:2608. 10635v1 Announce Type: cross Abstract: Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging.
arXiv:2608. 13690v1 Announce Type: cross Abstract: Medical image segmentation is still largely treated as a vision-only problem, although clinical interpretation often relies on textual knowledge of anatomy, location, appearance, and surrounding context.
arXiv:2609.37283v1 Announce Type: new Abstract: Medical multimodal large language models (MLLMs) are increasingly expected not only to answer clinical questions, but also to localize the visual evide...
Integrating 3D medical images with vision-language models (VLMs) holds substantial promise for computer-aided diagnosis. However, volumetric images generate prohibitively long visual-token sequences with considerable spatial and inter-slice redundancy.
arXiv:2609.23139v1 Announce Type: new Abstract: Most medical vision-language models (VLMs) excel at open-ended report generation and VQA but provide limited support for structured, fine-grained clini...
arXiv:2607.12896v3 Announce Type: replace Abstract: Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fr...
SEG-SAM is a unified medical image segmentation model that builds on the Segment Anything Model (SAM) by integrating semantic medical knowledge. It introduces a semantic‑aware decoder separate from SAM’s original decoder to handle both semantic segmentation of prompted objects and classification of unprompted objects. The model also incorporates key medical category characteristics from large language models via a text‑to‑vision semantic module and uses a cross‑mask spatial alignment strategy to improve overlap between predictions, achieving superior performance over existing SAM‑based and task‑specific methods.
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.
arXiv:2601.06847v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We po...
Textual descriptions can reduce ambiguity in medical image segmentation by specifying the finding and location to be delineated. Existing text-guided methods mainly improve where image and language fe...
BiCLIP is a bidirectional multimodal framework that enhances medical image segmentation by allowing visual features to iteratively refine textual representations, improving semantic alignment. It incorporates an augmentation consistency objective to stabilize learning against perturbed inputs. Experiments on QaTa-COV19 and MosMedData+ show that BiCLIP outperforms state‑of‑the‑art image‑only and multimodal baselines, achieving strong performance even with only 1% labeled data and resisting common clinical artifacts such as motion blur and low‑dose CT noise.
arXiv:2608. 09818v1 Announce Type: cross Abstract: Reliable medical image understanding requires models to connect clinical language and visual reasoning with pixel-level grounding.
Multimodal Routing and Region Refinement for Language-Guided Medical Image Segmentation (MRSeg) is a parameter‑efficient framework that uses frozen ConvNeXt‑Tiny and PubMedBERT encoders to extract multiscale visual features and clinical text tokens. A joint router predicts a sparse mixture over low‑rank adapter bases, enabling separate adaptation for two visual scales and text while keeping feature‑specific parameters distinct. Region Bridge aggregates dense visual tokens into latent regions using text‑derived queries, refines them via self‑attention and text cross‑attention, and redistributes the refined information back to the feature maps, culminating in a multiscale decoder that combines refined semantic features with shallow image evidence. MRSeg achieves state‑of‑the‑art Dice/mIoU scores on QaTa‑COV19 and MosMedData+ with only 7.11 M trainable parameters and 7.60 GFLOPs.