Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma Images

By Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, C. Cavallo, Xiaochun Zhao, Sirin Gandhi, L. Moreira, J. Eschbacher, Peter Nakaji, Mark C. Preul, Yezhou Yang. 2018

41 citations recorded by the source corpus.

Confocal Laser Endomicroscopy (CLE) is novel handheld fluorescence imaging technology that has shown promise for rapid intraoperative diagnosis of brain tumor tissue. Currently CLE is capable of image display only and lacks an automatic system to aid the surgeon in diagnostically analyzing the images. The goal of this project was to develop a computer-aided diagnostic approach for CLE imaging of human glioma with feature localization function. Despite the tremendous progress in object detection and image segmentation methods in recent years, most of such methods require large annotated datasets for training. However, manual annotation of thousands of histopathology images by physicians is costly and time consuming. To overcome this problem, we constructed a Weakly-Supervised Learning (WSL)-based model for feature localization that trains on image-level annotations, and then localizes incidences of a class-of-interest in the test image. We developed a novel convolutional neural network for diagnostic features localization from CLE images by employing a novel multiscale activation map that is laterally inhibited and collaterally integrated. To validate our method, we compared the model output to the manual annotation performed by four neurosurgeons on test images. The model achieved 88% mean accuracy and 86% mean intersection over union on intermediate features and 87% mean accuracy and 88% mean intersection over union on restrictive fine features, while outperforming other state of the art methods tested. This system can improve accuracy and efficiency in characterization of CLE images of glioma tissue during surgery, and may augment intraoperative decision-making regarding the tumor margin and improve brain tumor resection.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ala Al-Fuqaha
2. Junaid Qadir
3. Khansa Rasheed
4. Mohamed Ghaly
5. Adeel Razi
6. Jun Xia
7. Guang Yang
8. Qinghao Ye

In Computer Vision and Pattern Recognition

1. Ala Al-Fuqaha
2. Khansa Rasheed
3. Mohamed Ghaly
4. Junaid Qadir
5. Adeel Razi
6. Jun Xia
7. Guang Yang
8. Qinghao Ye

In Radiology, Nuclear Medicine and Imaging

1. Ala Al-Fuqaha
2. Junaid Qadir
3. Adeel Razi
4. Jun Xia
5. Guang Yang
6. Qinghao Ye
7. Adnan Qayyum
8. Shakked Noy

Citations in the corpus

• A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, neutral): “Likewise, see [57] [94].”
• Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond (2021, methodology): “[83] MLCAM Intrinsic Local Model-specific Br Zhao et al.”
• Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, methodology): “Izadyyazdanabadi et al. [106] integrated multiscale activation maps (MLCAM) with the CNN model to locate the attributes of glioma tumors. c) Layer-wise Relevance Propagation (LRP): This technique backpropagates the output decision to the input layer to estimate the relevance of each attribute.”