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arXiv Computer Vision

TLNM: Externally Validated Tooth Detection, Numbering and Segmentation from Smartphone Photographs Using Mask R-CNN

The paper presents TLNM, a Mask R‑CNN based system that detects, numbers, and segments teeth in smartphone photographs. It incorporates a masked gray‑world white‑balancing step and an anatomically constrained detection layer to handle patient‑generated variability. Evaluated on internal and external datasets, the model achieved high AP50, PQ, and F1 scores, demonstrating robust performance across diverse populations and imaging conditions.

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arXiv AI
Sep 17

Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning

The paper introduces a dual‑stage deep learning system for detecting dental caries in panoramic radiographs. It first localizes teeth using Faster R‑CNN, then applies U‑Net for pixel‑wise caries segmentation, converting polygon annotations into high‑resolution binary masks. Trained on 3,000 images with both expert and algorithmic labels, the model achieves an IoU of 0.9013, Dice of 0.9482, Recall of 0.9433, and Precision of 0.9774, outperforming existing methods and reducing false positives.

By Jihun Kim, Kyeonghun Kim, Jong-yeol Lee, Yeongseok Seo, Dohyun Chun
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arXiv AI
Aug 25

Robust Lightweight Deep Learning Models for Oral Cancer Screening

arXiv:2608.21583v1 Announce Type: new Abstract: Oral cancer is a leading cause of mortality in low-to-middle-income countries, where a shortage of specialists delays diagnosis. While point-of-care sc...

By Siddhant Bharadwaj, Aakash Shedsale, Tejashree Subramanya, Mohd. Azfar, Praveen Birur, Debnath Pal, Shankararama Sharma, Anupama Shetty, Rajesh Sundaresan
llmsefficiencybenchmarkssafetydata-engineering
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Hugging Face Trending Papers
Jul 14

ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations

Prototype-based networks provide inherently interpretable classification by linking predictions to learned exemplars, but their use in 3D point clouds and clinical surface-pair reasoning remains limited. We introduce ProtoPointNet, a prototype-based model for dental occlusion classification from registered upper--lower intraoral arch pairs.

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arXiv AI
4d ago

WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care

WoundAmbit is a framework that evaluates state‑of‑the‑art deep learning models for wound segmentation, benchmarking general‑purpose vision, medical imaging, and public wound challenge methods under standardized training, augmentation, and cross‑validation. The study finds transformer‑based TransNeXt offers the best generalizability, while all models run at least one image per second on a CPU and show strong interpretability and expert‑approved mask quality. The framework also introduces a reference‑object approach to convert masks into clinically relevant wound size estimates, demonstrating close alignment with expert annotations and integration into a custom telehealth system.

By Vanessa Borst, Timo Dittus, Tassilo Dege, Astrid Schmieder, Samuel Kounev
llmscomputer-visionbenchmarkssafety
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arXiv Machine Learning
Sep 4

Beyond Blur: A Semantic Tri-view Pipeline for Teledermatology Gradability via Skin Micro-relief

The paper introduces the Semantic Tri-view Pipeline, an interpretable system that automatically screens teledermatology photographs for gradability by analyzing epidermal micro-relief across up to three smartphone views. It uses a lightweight DeepLabV3+ model to segment micro-relief fidelity and aggregates the resulting spatial masks with logistic regression, leveraging viewpoint redundancy to improve robustness. Evaluated on the SCIN dataset, the approach raises the AUC from 0.81 to 0.96 on optically clear cases, offering real‑time, privacy‑by‑design feedback to filter ungradable photo sets before clinician review.

By Robert Engel
safety
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