Medical Image Object Detection

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11 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

PanoDent-KD: From Generalist Knowledge Distillation to Cross-Dataset Specialist Detection and Embedded Deployment in Panoramic Radiographs
Oct 18, 2026Muhammad Waqas Ashraf, Imad Ali Shah, Ali HassanMedical Image Object Detection Object Detection
QwenVLConnector: A Fast, Unified Medical VLM Chatbot for Fine-Grained Clinical Perception and Text Generation
Sep 19, 2026Le Thien Phuc Nguyen, Thien Nguyen, Thanh-Huy Nguyen +3Medical Image Object Detection Medical VLMs
University of Wisconsin - Madison, USA · University of North Carolina - Chapel Hill · Carnegie Mellon University, USA +3
Federated Learning Framework for Privacy-Preserving Kidney Stone Detection
Sep 17, 2026Najiyya Younas, Omar Abdulkader, Yaser Ali Shah +3Medical Imaging Medical Image Object Detection
Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan · Faculty of Computer Studies, Arab Open University, Riyadh, Saudi Arabia · Department of Artificial Intelligence, Kyungdong University Global, Toseong-myeon 24764, South Korea. +1
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
Aug 5, 2026Bin Pu, Jiewen Yang, Liwen Wang +9Medical Image Object Detection Ultrasound Imaging
Hunan University Changsha, Hunan, China · The Hong Kong University of Science and Technology HKSAR, China · Anhui University Hefei, Anhui, China +5
YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI
Aug 5, 2026Talha Waqas, Mounir Lahlouh, Kawther Taibouni +43D Medical Imaging Medical Image Localization
ESME Research Lab, Paris, France · LISSI (EA 3956), Université Paris-Est Créteil, Vitry-sur-Seine, France · LRE EPITA, Le Kremlin-Bicêtre, France +3
RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
Aug 1, 2026Kai Geissler, Laurens Müller-Groh, Hans Meine3D Object Detection 3D Medical Imaging
Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany
PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy
Jul 22, 2026Zübeyr Özeren, Tansel UyarMedical Image Object Detection Diabetic Retinopathy Grading
Department of Biomedical Engineering, Baskent University, Ankara, Türkiye · Department of Biomedical Engineering, Ankara University, Ankara, Türkiye
Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection
May 2, 2026Ceausescu Ciprian-Mihai, Anghelina Ion-Marian, Alexe Dumitru-BogdanMedical Image Object Detection Multi-Task Learning
Faculty of Mathematics and Computer Science, University of Bucharest, Str. Academiei, 14, Bucharest, 010014, Romania · Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Romania, Calea 13 Septembrie, 13, Bucharest, 050711, Romania
Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection
Apr 26, 2026Xuefen Liu, Xinquan Yang, Mianjie Zheng +5Medical Imaging Medical Image Analysis
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. · School of Artificial Intelligence, Shenzhen University, Shenzhen, China. · Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China.
EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms
Apr 23, 2026Brian VanVoorst, Nicholas Walczak, Christopher Gilleo +9Medical Image Object Detection Egocentric Video Understanding
RTX BBN Technologies · New York University · Northeastern University
Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels
Mar 16, 2026Victor Wåhlstrand, Jennifer Alvén, Ida HäggströmMedical Image Object Detection Object Detection
Chalmers University of Technology, SE-41296 Göteborg, Sweden

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PanoDent-KD: From Generalist Knowledge Distillation to Cross-Dataset Specialist Detection and Embedded Deployment in Panoramic Radiographs

Muhammad Waqas Ashraf, Imad Ali Shah, Ali Hassan

Medical Image Object Detection Object Detection Edge Inference Feature-Based Knowledge Distillation

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Reliable panoramic-radiograph detection requires consistency across datasets with different class definitions and deployment software. We present PanoDent-KD, an auditable 1280-pixel YOLO26m pipeline linking a 12-class source generalist, feature-level knowledge distillation (KD), exact classifier-row transfer, teacher-free five-class specialization, and embedded deployment. A common training and evaluation pipeline compares three source-training arms across three seeds: a supervised baseline (B1), single-teacher KD (STKD), and multi-teacher KD (MTKD). Five shared classifier rows initialize nine OralXrays-9 specialists, while the backbone, FPN-PAN neck, and dual-detection-head topology are retained unchanged. On the 2,686-image test subset, all three arms achieve macro box F1 near 0.82. B1 achieves the highest three-seed arm-level mean macro box F1 (0.8239

\pm

0.0027) and

mAP_{50:95}

(0.6130

\pm

0.0067). STKD seed 1, selected after development but before final testing, was used for deployment. Its Jetson Orin Nano Super FP16 TensorRT engine achieves 77.03-ms median and 80.86-ms P95 prediction-call latency, throughput of 12.90 images/s, and gross total-module energy of 1.39 J/image, with high numerical agreement between within-device PyTorch and TensorRT FP16 inference. These findings demonstrate the feasibility of an auditable, device-specific deployment workflow, but do not establish clinical validity or improved target-domain accuracy from KD.