ACM Multimedia Grand Challenge on ENT Endoscopy Analysis

• URL: http://arxiv.org/abs/2508.04801v1
• Date: Wed, 06 Aug 2025 18:22:23 GMT
• Title: ACM Multimedia Grand Challenge on ENT Endoscopy Analysis
• Authors: Trong-Thuan Nguyen, Viet-Tham Huynh, Thao Thi Phuong Dao, Ha Nguyen Thi, Tien To Vu Thuy, Uyen Hanh Tran, Tam V. Nguyen, Thanh Dinh Le, Minh-Triet Tran,
• Abstract summary: We introduce ENTRep, which integrates fine-grained anatomical classification with image-to-image and text-to-image retrieval under bilingual supervision.<n>The dataset comprises expert-annotated images, labeled for anatomical region and normal or abnormal status, and accompanied by dual-language narrative descriptions.
• Score: 9.343316855950263
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated analysis of endoscopic imagery is a critical yet underdeveloped component of ENT (ear, nose, and throat) care, hindered by variability in devices and operators, subtle and localized findings, and fine-grained distinctions such as laterality and vocal-fold state. In addition to classification, clinicians require reliable retrieval of similar cases, both visually and through concise textual descriptions. These capabilities are rarely supported by existing public benchmarks. To this end, we introduce ENTRep, the ACM Multimedia 2025 Grand Challenge on ENT endoscopy analysis, which integrates fine-grained anatomical classification with image-to-image and text-to-image retrieval under bilingual (Vietnamese and English) clinical supervision. Specifically, the dataset comprises expert-annotated images, labeled for anatomical region and normal or abnormal status, and accompanied by dual-language narrative descriptions. In addition, we define three benchmark tasks, standardize the submission protocol, and evaluate performance on public and private test splits using server-side scoring. Moreover, we report results from the top-performing teams and provide an insight discussion.

Related papers

• PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging [6.411386758550256]
  PRS-Med is a framework that integrates vision-language models with segmentation capabilities to generate both accurate segmentation masks and corresponding spatial reasoning outputs.<n> MMRS dataset provides diverse, spatially-grounded question-answer pairs to address the lack of position reasoning data in medical imaging.
  arXiv  Detail & Related papers  (2025-05-17T06:42:28Z)
• Anatomy-Aware Conditional Image-Text Retrieval [29.872292146073207]
  Image-Text Retrieval (ITR) finds broad applications in healthcare, aiding clinicians and radiologists by automatically retrieving relevant patient cases.<n>We propose an Anatomical Location-Conditioned Image-Text Retrieval framework, which aims to retrieve similar patient cases in the same anatomical region.<n>We show that our proposed RRA-VL achieves state-of-the-art localization performance in phase-grounding tasks.
  arXiv  Detail & Related papers  (2025-03-10T15:36:49Z)
• RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining [64.66825253356869]
  We propose a novel methodology that leverages dense radiology reports to define image-wise similarity ordering at multiple granularities.<n>We construct two comprehensive medical imaging retrieval datasets: MIMIC-IR for Chest X-rays and CTRATE-IR for CT scans.<n>We develop two retrieval systems, RadIR-CXR and model-ChestCT, which demonstrate superior performance in traditional image-image and image-report retrieval tasks.
  arXiv  Detail & Related papers  (2025-03-06T17:43:03Z)
• Text-Promptable Propagation for Referring Medical Image Sequence Segmentation [20.724643106195852]
  Ref-MISS aims to segment anatomical structures in medical image sequences based on natural language descriptions.<n>Existing 2D and 3D segmentation models struggle to explicitly track objects of interest across medical image sequences.<n>We propose Text-Promptable Propagation (TPP), a model designed for referring medical image sequence segmentation.
  arXiv  Detail & Related papers  (2025-02-16T12:13:11Z)
• Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images [30.673958586581904]
  We introduce FLanS, a novel medical image segmentation model that handles various free-form text prompts.<n> FLanS is trained on a large-scale dataset of over 100k medical images from 7 public datasets.
  arXiv  Detail & Related papers  (2024-10-02T16:34:32Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)
• Anatomical Structure-Guided Medical Vision-Language Pre-training [21.68719061251635]
  We propose an Anatomical Structure-Guided (ASG) framework for learning medical visual representations. For anatomical region, we design an automatic anatomical region-sentence alignment paradigm in collaboration with radiologists. For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample.
  arXiv  Detail & Related papers  (2024-03-14T11:29:47Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation [107.3538598876467]
  We propose an Auxiliary Signal-Guided Knowledge-Decoder (ASGK) to mimic radiologists' working patterns. ASGK integrates internal visual feature fusion and external medical linguistic information to guide medical knowledge transfer and learning.
  arXiv  Detail & Related papers  (2020-06-06T01:00:15Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.