A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images

Hu, Ziwei; Deng, Yansha; Lan, Junlin; Wang, Tao; Han, Zixin; Huang, Yuxiu; Zhang, Hejun; Wang, Jianchao; Cheng, Musheng; Jiang, Haiyan; Lee, Ren-Guey; Du, Min; Tong, Tong; Gao, Qinquan; Chen, Gang

doi:10.1016/j.bspc.2022.104261

Cited by 14 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4.4.1. Object Detection Evaluation Index mAP50, FPS, params, and Gflops are indicators to measure the performance of object detection models [35]. mAP50 (mean average precision at 50) is an indicator used to measure the performance of the object detection algorithm, indicating the average accuracy of the matching degree between the detected object and the real object, where 50 means the IoU (intersection-over-union ratio) threshold is 0.5 [36].…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

et al. 2023

View full text Add to dashboard Cite

The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.

show abstract

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Based on this, many derived metrics can be used to evaluate the difference between segmentation results and ground truth results. In our experimental evaluation, we use commonly used derived metrics such as Precision, Dice, MIoU, and Sensitivity [32] as evaluation metrics.…”

Section: Segmentation Evaluation Metricsmentioning

confidence: 99%

Multilevel Feature Extraction Model for Lung Vessel Segmentation in CT Images with an Emphasis on Small Vessel

Liao,

彭,

Tang

et al. 2023

Preprint

View full text Add to dashboard Cite

Lung vessel segmentation is essential for diagnosing and treating lung diseases. Most existing research relies on direct image segmentation, making it challenging to accurately segment small vessels in lung CT images. Hence, we introduced a multilevel feature extraction model for lung vessel segmentation in CT images. We extract the structural features of lung CT images as prior information to enhance segmentation accuracy, combining convolutional, MLP, and Transformer techniques to extract features at different image levels. We also refined the attention mechanism to reallocate computational resources to target regions. Additionally, we adjusted the loss function, dynamically setting the loss weight based on the size of the segmented target in the label, enhancing the segmentation precision of small vessels. Compared to current mainstream methods, our model's modules significantly contribute to the segmentation results, particularly for small vessels, with metrics improving by over 2%. It also excels in other medical image segmentation tasks.

show abstract

“…Thus, automatic segmentation of the ROI is of great significance for omics research. Hu Z et al proposed a multi-task deep learning framework for automatic segmentation of gastric cancer in human tissue sections using whole slide images (WSI) (40). Zhang Y et al presented a 3D multi-attention-guided multi-…”

Section: Preoperative Diagnosis Of Gc Has Been a Focal Point Of Researchmentioning

confidence: 99%

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Gao,

Yu,

Zhang

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

show abstract

A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images

Cited by 14 publications

References 41 publications

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

Multilevel Feature Extraction Model for Lung Vessel Segmentation in CT Images with an Emphasis on Small Vessel

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

Contact Info

Product

Resources

About