EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer

• URL: http://arxiv.org/abs/2201.04795v1
• Date: Thu, 13 Jan 2022 05:24:40 GMT
• Title: EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer
• Authors: Jiaqiao Shi, Aleksandar Vakanski, Min Xian, Jianrui Ding, Chunping Ning
• Abstract summary: 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.
• Score: 58.720142291102135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask 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. Moreover, we propose a new numerically stable loss function that easily controls the balance between the sensitivity and specificity of cancer detection. The proposed approach is evaluated using a breast ultrasound dataset with 1,511 images. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively. We validate the model using a virtual mobile device, and the average inference time is 0.35 seconds per image.

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)
• Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI [2.9746083684997418]
  This study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. A Convolutional Neural Network (CNN) architecture is employed for tumor classification. 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach.
  arXiv  Detail & Related papers  (2024-05-07T05:55:50Z)
• Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example [40.3927727959038]
  This paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images. It enables the rapid and automatic classification of pathological images into benign and malignant groups. It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer.
  arXiv  Detail & Related papers  (2024-04-12T07:08:05Z)
• A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification [4.371891660358126]
  This paper introduces a novel deep-learning approach for improved breast cancer classification. Our method hinges on the Residual Dual-Shuffle Attention Network (DRDA-Net), inspired by ShuffleNet's efficient architecture. MobileNet ensures fast execution even on devices with limited resources without sacrificing performance.
  arXiv  Detail & Related papers  (2024-03-17T08:09:48Z)
• Convolutional neural network based on transfer learning for breast cancer screening [0.0]
  In this paper, a deep convolutional neural network-based algorithm is proposed to aid in accurately identifying breast cancer from ultrasonic images. Several experiments were conducted on the breast ultrasound dataset consisting of 537 Benign, 360 malignant, and 133 normal images. Using k-fold cross-validation and a bagging ensemble, we achieved an accuracy of 99.5% and a sensitivity of 99.6%.
  arXiv  Detail & Related papers  (2021-12-22T02:27:12Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization [45.00998416720726]
  We propose a framework to address the unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a final prediction.
  arXiv  Detail & Related papers  (2020-02-13T15:28:42Z)
• A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images [21.627818410241552]
  A novel and efficient pancreatic tumor detection framework is proposed in this paper. The contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation Module. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge.
  arXiv  Detail & Related papers  (2020-02-11T15:48:22Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

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.