SCALE-VLP: Soft-Weighted Contrastive Volumetric Vision-Language Pre-training with Spatial-Knowledge Semantics

• URL: http://arxiv.org/abs/2511.02996v1
• Date: Tue, 04 Nov 2025 21:03:17 GMT
• Title: SCALE-VLP: Soft-Weighted Contrastive Volumetric Vision-Language Pre-training with Spatial-Knowledge Semantics
• Abstract summary: Existing approaches often treat scans as independent 2D slices, compromising spatial coherence and underutilizing clinical semantics.<n>We propose a soft-weighted contrastive vision-language-training framework that integrates spatial semantics and domain-aware representations.<n>This yields structurally consistent and semantically grounded representations underg supervision, demonstrating cross-task transferability (retrieval, report generation, and classification) and cross-domain generalizability with consistent gains without further fine-tuning.
• Score: 31.49282177777596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have demonstrated strong cross-modal capabilities, yet most work remains limited to 2D data and assumes binary supervision (i.e., positive vs. negative pairs), overlooking the continuous and structured dependencies present in volumetric data such as CT. Existing approaches often treat volumetric scans as independent 2D slices, compromising spatial coherence and underutilizing rich clinical semantics. We propose SCALE-VLP, a soft-weighted contrastive vision-language pre-training framework that integrates (i) volumetric spatial semantics to preserve anatomical structure and (ii) domain-aware, knowledge-infused semantics (e.g., radiological ontologies) to guide alignment. This yields structurally consistent and semantically grounded representations under limited supervision, demonstrating strong cross-task transferability (retrieval, report generation, and classification), and cross-domain generalizability with consistent gains without further fine-tuning. In particular, compared to the previous state of the art, SCALE-VLP achieves up to 4.3x higher top-1 CT-report retrieval, improves abnormality classification by 10 points, and reaches ROUGE-L 0.44 and BERT-F1 0.89 for report generation. Further, in zero-shot evaluation on an out-of-domain external dataset, we observe consistent gains, indicating the cross-task and cross-domain generalization ability of SCALE-VLP.

Related papers

• Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images [48.646448077284276]
  We propose Spatial-FAD, a framework for effective few-shot medical anomaly detection.<n>It improves lesion localization by combining VFM spatial priors with CLIP semantics.<n>Our method achieves an average improvement of over 11.4% in Dice score and 1.8% in AUC.
  arXiv  Detail & Related papers  (2026-09-11T05:21:01Z)
• AlphaRAD: Grounded Zero-Shot Classification in Chest Radiology via $α$-Corrected Binary Cross Entropy and Factorized Latent Supervision [5.2813369503136744]
  We introduce AlphaRAD, a large-scale structured medical concept space from medical reports parsed by a Large Language Model for training.<n>We also propose FLaS, an extremely simple yet effective cross-modal feature fusion module that factorizes VLPM representations into independent subspaces.<n>AlphaRAD shows strong zero-shot generalization across diverse chest radiology tasks.
  arXiv  Detail & Related papers  (2026-09-01T18:24:50Z)
• LoRCA: LoRA Cycle Adaptation for Histology to HiP-CT Translation with DINOv3 [3.882636699866806]
  We propose a cycle consistent style translation framework built on a shared frozen DINOv3 with modality-specific LoRA adapters.<n>We evaluate translation quality using Fréchet Inception Distance (FID) and structural fidelity via mutual information.<n>As a preliminary indicator of downstream registration utility, we find that style-translated images yield increased feature correspondences under MatchAnything on manually aligned HiP-CT and histology test pairs.
  arXiv  Detail & Related papers  (2026-08-07T15:43:55Z)
• Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis [3.616534682321388]
  We propose the dataset-informed transfer learning framework, which integrates dataset-derived difficulty signals with neighborhood-based triplet supervision.<n>DITL achieves state-of-the-art performance for whole-image breast density classification, with significant improvements across accuracy, F1-score, and AUC.<n>By bridging small-scale lesion analysis with large-scale density estimation, DITL establishes a clinically relevant, scalable, and generalizable framework for mammography classification.
  arXiv  Detail & Related papers  (2026-07-28T17:52:36Z)
• Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation [54.74045834035952]
  Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs.<n>We propose a rank-aware hyperbolic alignment (RAHA) that combines hierarchical geometry with explicit alignment-capacity control.<n>RAHA demonstrates competitive cross-modal retrieval and improved transfer indicators under fixed budgets.
  arXiv  Detail & Related papers  (2026-06-28T15:41:31Z)
• Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography [75.7789001619107]
  Vision-language pre-training holds great promise for general-purpose medical AI.<n>We propose a tailored framework featuring three key components.<n>A diagnosis-aware prompt strategy employs real clinical phrases and aggregated disease prototypes to bridge the pre-training-inference gap.
  arXiv  Detail & Related papers  (2026-06-24T08:24:45Z)
• PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT [8.358409792893278]
  We develop a domain-adaptive transformer segmentation framework tailored for multi-institutional Quantitative computed tomography (QCT)<n>Our model is trained and validated on one of the largest hip fracture related research cohorts to date, comprising 1,024 QCT images scans from Tulane University and 384 scans from Rochester, Minnesota for proximal femur segmentation.
  arXiv  Detail & Related papers  (2025-10-30T18:07:56Z)
• Enhancing CLIP Robustness via Cross-Modality Alignment [54.01929554563447]
  We propose Cross-modality Alignment, an optimal transport-based framework for vision-language models.<n> COLA restores global image-text alignment and local structural consistency in the feature space.<n> COLA is training-free and compatible with existing fine-tuned models.
  arXiv  Detail & Related papers  (2025-10-28T03:47:44Z)
• S-Chain: Structured Visual Chain-of-Thought For Medicine [81.97605645734741]
  We introduce S-Chain, the first large-scale dataset of 12,000 expert-annotated medical images with bounding boxes and structured visual CoT (SV-CoT)<n>The dataset further supports 16 languages, totaling over 700k VQA pairs for broad multilingual applicability.<n>S-Chain establishes a new benchmark for grounded medical reasoning and paves the way toward more trustworthy and explainable medical vision-language models.
  arXiv  Detail & Related papers  (2025-10-26T15:57:14Z)
• XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography [6.447908430647854]
  We present the first systematic benchmark for evaluating cross-modal interpretability in chest X-rays.<n>We generate visual explanations using cross-attention and similarity-based localization maps.<n>We quantitatively assess their alignment with radiologist-annotated regions across multiple pathologies.
  arXiv  Detail & Related papers  (2025-10-22T13:52:19Z)
• Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation [61.350584471060756]
  Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images.<n>We propose Self-Supervised Anatomical Consistency Learning (SS-ACL) to align generated reports with corresponding anatomical regions.<n>SS-ACL constructs a hierarchical anatomical graph inspired by the invariant top-down inclusion structure of human anatomy.
  arXiv  Detail & Related papers  (2025-09-30T08:59:06Z)
• RAU: Reference-based Anatomical Understanding with Vision Language Models [26.06602931463068]
  We introduce RAU, a framework for reference-based anatomical understanding with vision-language models (VLMs)<n>We first show that a VLM learns to identify anatomical regions through relative spatial reasoning between reference and target images.<n>Next, we demonstrate that the VLM-derived spatial cues can be seamlessly integrated with the fine-grained segmentation capability of SAM2.
  arXiv  Detail & Related papers  (2025-09-26T14:32:03Z)
• Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception [71.26728044621458]
  DeCLIP is a novel framework that enhances CLIP by decoupling the self-attention module to obtain content'' and context'' features respectively.<n>It consistently achieves state-of-the-art performance across a broad spectrum of tasks, including 2D detection and segmentation, 3D instance segmentation, video instance segmentation, and 6D object pose estimation.
  arXiv  Detail & Related papers  (2025-08-15T06:43:51Z)
• Learning from Heterogeneous Structural MRI via Collaborative Domain Adaptation for Late-Life Depression Assessment [24.340328016766183]
  We propose a Collaborative Domain Adaptation framework for LLD detection using T1-weighted MRIs.<n>The framework consists of three stages: supervised training on labeled source data, self-supervised target feature adaptation and collaborative training on unlabeled target data.<n>Experiments conducted on multi-site T1-weighted MRI data demonstrate that the framework consistently outperforms state-of-the-art unsupervised domain adaptation methods.
  arXiv  Detail & Related papers  (2025-07-30T01:38:32Z)
• LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance [54.683384204063934]
  Large multi-modal models (LMMs) struggle with inaccurate segmentation and hallucinated comprehension.<n>We propose LIRA, a framework that capitalizes on the complementary relationship between visual comprehension and segmentation.<n>LIRA achieves state-of-the-art performance in both segmentation and comprehension tasks.
  arXiv  Detail & Related papers  (2025-07-08T07:46:26Z)
• Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation [14.536384387956527]
  We develop a novel Multi-Scale Cross Supervised Contrastive Learning framework to segment structures in medical images. Our approach contrasts multi-scale features based on ground-truth and cross-predicted labels, in order to extract robust feature representations. It outperforms state-of-the-art semi-supervised methods by more than 3.0% in Dice.
  arXiv  Detail & Related papers  (2023-06-25T16:55:32Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.