HSQ-VLM: A Novel Spatially-Constrained Quadrant Segmentation VLM Model for Explainability in Diabetic Retinopathy

• URL: http://arxiv.org/abs/2606.14803v1
• Date: Thu, 11 Jun 2026 19:02:02 GMT
• Title: HSQ-VLM: A Novel Spatially-Constrained Quadrant Segmentation VLM Model for Explainability in Diabetic Retinopathy
• Abstract summary: We propose HSQ-VLM, a novel quadrant segmentation pipeline on fundus images.<n>Unlike traditional methods that rely on arbitrary image partitioning, our pipeline implements 4-quadrant Topological Latent Partitioning.<n>This allows the Vision-Language Model to generate natural language reports that quantify pathology with anatomical precision.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetic Retinopathy (DR) is an aggressive retinal disease and a leading cause of global blindness, yet its clinical management is currently hindered by the black-box nature of diagnostic AI. While deep learning models achieve high classification accuracy, there is a critical lack of explainability methods capable of detailing the exact anatomical landmarks and lesion distributions that lead to a clinical decision for DR. Therefore, we propose HSQ-VLM, a novel quadrant segmentation pipeline on fundus images that utilizes a Landmark-Anchored Cartesian Cross-Attention mechanism to unify visual feature extraction with structured clinical reasoning. Unlike traditional methods that rely on arbitrary image partitioning, our pipeline implements 4-quadrant Topological Latent Partitioning (TLP) to dynamically align retinal features with a fovea-centered coordinate system. This allows the Vision-Language Model to generate natural language reports that quantify pathology with anatomical precision. On a dataset of 3,500 high-resolution fundus images, this innovative methodology achieved a lesion detection sensitivity of 99.6% for hemorrhages and 96.4% for microaneurysms, while demonstrating a significant reduction in boundary-ambiguity errors compared to standard segmentation baselines.

Related papers

• Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence [51.57735743553791]
  We introduce FundusGround, a new benchmark for clinically interpretable ophthalmic VQA with spatially-grounded lesion evidence.<n>Built upon this structured lesion evidence, 72,706 questions are then generated spanning four formats.<n>Experiments demonstrate that incorporating lesion-level visual evidence consistently improves model performance and transparency.
  arXiv  Detail & Related papers  (2026-05-21T12:37:03Z)
• Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography [4.828654519487229]
  unsupervised anomaly detection framework learns the normative distribution of healthy retinal anatomy without lesion annotations.<n>We incorporate retinal layer-aware supervision and structured triplet learning to separate healthy from pathological representations.<n>On the Kermany dataset (AUROC: 0.799), our method substantially outperforms VAE, VQVAE, VQGAN, and f-AnoGAN baselines.
  arXiv  Detail & Related papers  (2026-04-24T01:09:05Z)
• Semantic-Topological Graph Reasoning for Language-Guided Pulmonary Screening [9.742485458627343]
  We propose a novel Semantic-Topological Graph Reasoning framework for language-guided pulmonary screening.<n>Our approach elegantly synergizes the reasoning capabilities of large language models with the zero-shot delineation of vision foundation models.<n>It achieves an 81.5% Dice Similarity Coefficient (DSC) on LIDC-IDRI, outperforming leading LLM-based tools like LISA by over 5%.
  arXiv  Detail & Related papers  (2026-04-07T09:20:55Z)
• An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis [1.2871968485402088]
  Existing vision-language models fail to address the extreme class imbalance prevalent in clinical segmentation datasets.<n>We present an end-to-end Explainable Vision-Language Model framework designed to overcome these limitations.<n>We show considerable performance: a diagnostic classification accuracy of 90.69%, a macro-averaged Dice score of 0.9512 for segmentation, and a CIDEr score of 92.80%.
  arXiv  Detail & Related papers  (2026-04-02T20:18:35Z)
• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data [76.89269238957593]
  Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views.<n>We propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification.
  arXiv  Detail & Related papers  (2026-02-02T13:07:52Z)
• Quadrant Segmentation VLM with Few-Shot Adaptation and OCT Learning-based Explainability Methods for Diabetic Retinopathy [0.0]
  Diabetic Retinopathy (DR) is a leading cause of vision loss worldwide, requiring early detection to preserve sight.<n>Current AI models utilize lesion segmentation for interpretability; however, manually annotating lesions is impractical for clinicians.<n>This paper presents a novel multimodal explainability model utilizing a VLM with few-shot learning, which mimics an ophthalmologist's reasoning by analyzing lesion distributions within retinal quadrants for fundus images.
  arXiv  Detail & Related papers  (2025-12-20T17:45:33Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation [46.57880203321858]
  We propose a novel network (KaLDeX) for vascular segmentation leveraging a Kalman filter based linear deformable cross attention (LDCA) module. Our approach is based on two key components: Kalman filter (KF) based linear deformable convolution (LD) and cross-attention (CA) modules. The proposed method is evaluated on retinal fundus image datasets (DRIVE, CHASE_BD1, and STARE) as well as the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-10-28T16:00:42Z)
• Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network [4.583480375083946]
  Using classification or segmentation models on medical to learn latent features opt out robust feature selection and may lead to overfitting. We propose a novel feature selection technique using the latent space of a segmentation model that can aid diagnosis. Our approach shows promising results in differential diagnosis of a rare cardiac disease with 82% diagnosis accuracy beating the previous state-of-the-art (SOTA) approach.
  arXiv  Detail & Related papers  (2023-12-19T22:53:32Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)

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.