AlphaDent: A dataset for automated tooth pathology detection
- URL: http://arxiv.org/abs/2507.22512v1
- Date: Wed, 30 Jul 2025 09:34:43 GMT
- Title: AlphaDent: A dataset for automated tooth pathology detection
- Authors: Evgeniy I. Sosnin, Yuriy L. Vasilev, Roman A. Solovyev, Aleksandr L. Stempkovskiy, Dmitry V. Telpukhov, Artem A. Vasilev, Aleksandr A. Amerikanov, Aleksandr Y. Romanov,
- Abstract summary: This dataset is based on the DSLR camera photographs of the teeth of 295 patients and contains over 1200 images.<n>The article provides a detailed description of the dataset and the labeling format.<n>The results obtained show high quality of predictions.
- Score: 98.1937495272719
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In this article, we present a new unique dataset for dental research - AlphaDent. This dataset is based on the DSLR camera photographs of the teeth of 295 patients and contains over 1200 images. The dataset is labeled for solving the instance segmentation problem and is divided into 9 classes. The article provides a detailed description of the dataset and the labeling format. The article also provides the details of the experiment on neural network training for the Instance Segmentation problem using this dataset. The results obtained show high quality of predictions. The dataset is published under an open license; and the training/inference code and model weights are also available under open licenses.
Related papers
- Kvasir-VQA: A Text-Image Pair GI Tract Dataset [4.250633109741797]
This dataset comprises 6,500 annotated images spanning various GI tract conditions and surgical instruments.
The dataset is intended for applications such as image captioning, Visual Question Answering (VQA), text-based generation of synthetic medical images, object detection, and classification.
arXiv Detail & Related papers (2024-09-02T19:41:59Z) - Toffee: Efficient Million-Scale Dataset Construction for Subject-Driven Text-to-Image Generation [58.09421301921607]
We construct the first large-scale dataset for subject-driven image editing and generation.
Our dataset is 5 times the size of previous largest dataset, yet our cost is tens of thousands of GPU hours lower.
arXiv Detail & Related papers (2024-06-13T16:40:39Z) - HICH Image/Text (HICH-IT): Comprehensive Text and Image Datasets for
Hypertensive Intracerebral Hemorrhage Research [12.479936404475803]
We introduce a new dataset in the medical field of hypertensive intracerebral hemorrhage (HICH) called HICH-IT.
This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of HICH.
arXiv Detail & Related papers (2024-01-29T07:44:09Z) - NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in
H&E-Stained Histological Images [1.1500025852056222]
We release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg.
This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs.
For the first time, we provide additional ambiguous area masks for the entire dataset.
arXiv Detail & Related papers (2023-08-03T13:45:07Z) - Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data
Augmentation [42.034896915716374]
This paper presents a StyleGAN-driven approach for segmenting publicly available large medical datasets.
Style transfer is used to augment the training dataset and generate new anatomically sound images.
The augmented dataset is then used to train a U-Net segmentation network which displays a significant improvement in the segmentation accuracy.
arXiv Detail & Related papers (2023-02-07T06:34:10Z) - Applied Deep Learning to Identify and Localize Polyps from Endoscopic
Images [0.0]
We have aimed at open sourcing a dataset which contains annotations of polyps and ulcers.
This is the first dataset that's coming from India containing polyp and ulcer images.
We evaluated our dataset with several popular deep learning object detection models that's trained on large publicly available datasets.
arXiv Detail & Related papers (2023-01-22T22:14:25Z) - Computer-aided Tuberculosis Diagnosis with Attribute Reasoning
Assistance [58.01014026139231]
We propose a new large-scale tuberculosis (TB) chest X-ray dataset (TBX-Att)
We establish an attribute-assisted weakly-supervised framework to classify and localize TB by leveraging the attribute information.
The proposed model is evaluated on the TBX-Att dataset and will serve as a solid baseline for future research.
arXiv Detail & Related papers (2022-07-01T07:50:35Z) - Prefix Conditioning Unifies Language and Label Supervision [84.11127588805138]
We show that dataset biases negatively affect pre-training by reducing the generalizability of learned representations.
In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
arXiv Detail & Related papers (2022-06-02T16:12:26Z) - Self-Supervised Learning as a Means To Reduce the Need for Labeled Data
in Medical Image Analysis [64.4093648042484]
We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies.
We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
arXiv Detail & Related papers (2022-06-01T09:20:30Z) - ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised
Medical Image Segmentation [99.90263375737362]
We propose ATSO, an asynchronous version of teacher-student optimization.
ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset.
We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
arXiv Detail & Related papers (2020-06-24T04:05:12Z)
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.