Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training
- URL: http://arxiv.org/abs/2108.08158v3
- Date: Thu, 25 Jul 2024 12:52:52 GMT
- Title: Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training
- Authors: Hideaki Okamoto, Takakiyo Nomura, Kazuhito Nabeshima, Jun Hashimoto, Hitoshi Iyatomi,
- Abstract summary: The sensitivity (SE) of the proposed system for gastric cancer (90.2%) is higher than that of the expert (85.5%)
Two out of five candidates detected box are cancerous, achieving a high precision while maintaining a high processing speed of 0.51 seconds/image.
- Score: 1.9515126654284938
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but because it must be performed by a physician, the number of people who can be diagnosed is limited. Gastric X-ray, on the other hand, can be performed by technicians and can screen a much larger number of patients than endoscopy, but its correct diagnosis requires experience. We propose an unprecedented and practical gastric cancer diagnosis support system for gastric X-ray images, which will enable more people to be screened. The system is based on a general deep learning-based object detection model and includes two novel technical proposals: refined probabilistic stomach image augmentation (R-sGAIA) and hard boundary box learning (HBBT). R-sGAIA is a probabilistic gastric fold region enhancement method that provides more learning patterns for cancer detection models. HBBT is an efficient training method for object detection models that allows the use of unannotated negative (i.e., healthy control) samples that cannot be used for training in conventional detection models, thereby improving model performance. The sensitivity (SE) of the proposed system for gastric cancer (90.2%) is higher than that of the expert (85.5%), and two out of five candidates detected box are cancerous, achieving a high precision while maintaining a high processing speed of 0.51 seconds/image. The proposed system showed 5.9 points higher on the F1 score compared to methods using the same object detection model and state-of-the-art data augmentation. In short, the system quickly and efficiently shows the radiologist where to look, greatly reducing the radiologist's workload.
Related papers
- Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports [68.39938936308023]
We propose a novel text-guided learning method to achieve highly accurate cancer detection results.
Our approach can leverage clinical knowledge by large-scale pre-trained VLM to enhance generalization ability.
arXiv Detail & Related papers (2024-05-23T07:03:38Z) - BreastRegNet: A Deep Learning Framework for Registration of Breast
Faxitron and Histopathology Images [0.05454343470301196]
This study introduces a deep learning-based image registration approach trained on mono-modal synthetic image pairs.
The models were trained using data from 50 women who received neoadjuvant chemotherapy and underwent surgery.
arXiv Detail & Related papers (2024-01-18T08:23:29Z) - Improving the diagnosis of breast cancer based on biophysical ultrasound
features utilizing machine learning [0.0]
We propose a biophysical feature based machine learning method for breast cancer detection.
The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve.
arXiv Detail & Related papers (2022-07-13T23:53:09Z) - Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule
Diagnosis [20.510918720980467]
Lung cancer has the highest mortality rate of deadly cancers in the world.
Computer-aided diagnosis (CAD) systems have been developed to assist radiologists in nodule detection and diagnosis.
Lack of model reliability and interpretability remains a major obstacle for its large-scale clinical application.
arXiv Detail & Related papers (2022-04-08T08:21:00Z) - Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology:
AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate.
We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
arXiv Detail & Related papers (2022-02-17T08:33:52Z) - EMT-NET: Efficient multitask network for computer-aided diagnosis of
breast cancer [58.720142291102135]
We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously.
We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions.
The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
arXiv Detail & Related papers (2022-01-13T05:24:40Z) - Variational Knowledge Distillation for Disease Classification in Chest
X-Rays [102.04931207504173]
We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays.
We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
arXiv Detail & Related papers (2021-03-19T14:13:56Z) - Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for
Thoracic Disease Identification [83.6017225363714]
deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance.
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
arXiv Detail & Related papers (2021-02-26T02:29:30Z) - Automatic Generation of Interpretable Lung Cancer Scoring Models from
Chest X-Ray Images [9.525711971667679]
Lung cancer is the leading cause of cancer death worldwide.
Deep learning techniques are effective at automatically diagnosing lung cancer.
These techniques have yet to be clinically approved and adopted by the medical community.
arXiv Detail & Related papers (2020-12-10T04:11:59Z) - Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo
Hyperspectral Tumor Type Classification [49.32653090178743]
We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning.
Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
arXiv Detail & Related papers (2020-07-02T12:00:53Z) - Review on Computer Vision in Gastric Cancer: Potential Efficient Tools
for Diagnosis [0.0]
This review focuses on advances in computer vision on gastric cancer.
Different methods for data generation and augmentation are presented.
Classification and segmentation techniques are discussed for assisting more precise diagnosis and timely treatment.
arXiv Detail & Related papers (2020-05-17T16:14:15Z)
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.