Papers with medical image analysis
DE-CLIP: Few-Shot Anomaly Detection via Difference-Guided Embedding Editing (2026.acl-long)
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| Challenge: | Existing approaches to detect anomalies are limited due to the lack of anomalous samples . |
| Approach: | They propose a framework that edits text embeddings based on the differences between normal and anomalous samples. |
| Outcome: | The proposed framework achieves 96.6% and 96.99% AUROC on MVTec datasets. |
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation (2021.acl-long)
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| Challenge: | Existing methods for medical image analysis use predefined template databases or ignore hierarchical nature of medical report generation. |
| Approach: | They propose a hierarchical retrieval mechanism to extract both report and sentence-level templates for clinically accurate report generation. |
| Outcome: | The proposed model extracts both report and sentence-level templates for clinically accurate report generation. |
Libra: Leveraging Temporal Images for Biomedical Radiology Analysis (2025.findings-acl)
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| Challenge: | Existing methods for radiology report generation rely on single-image analysis or rule-based heuristics to process multiple images. |
| Approach: | They propose a temporal-aware MLLM tailored for chest X-ray report generation that combines a radiology-specific image encoder with a novel Temporal Alignment Connector. |
| Outcome: | The proposed model sets new standards in clinical relevance and lexical accuracy on the MIMIC-CXR dataset. |
Look & Mark: Leveraging Radiologist Eye Fixations and Bounding boxes in Multimodal Large Language Models for Chest X-ray Report Generation (2025.findings-acl)
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| Challenge: | Recent advances in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, but still suffer from hallucinations and clinically significant errors. |
| Approach: | They propose a grounding fixation strategy that integrates radiologist eye fixations and bounding box annotations into the LLM prompting framework. |
| Outcome: | The proposed model improves performance without retraining across domain-specific and general-purpose models and achieves an 87.3% clinical average performance. |