Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.

Lung cancer is one of the most common causes of death among all cancer-related diseases (Cancer Research UK in Cancer mortality for common cancers. http://www.cancerresearchuk.org/health-professional/cancer-statistics/mortality/common-cancers-compared
 , 2017). It is primarily diagnosed by performing a scan analysis of the patient’s lung. This scan analysis could be of X-ray, CT scan, or MRI. Automated classification of lung cancer is one of the difficult tasks, attributing to the varying mechanisms used for imaging patient’s lungs. Image processing and machine learning approaches have shown a great potential for detection and classification of lung cancer. In this paper, we have demonstrated effective approach for detection and classification of lung cancer-related CT scan images into benign and malignant category. Proposed approach firstly processes these images using image processing techniques, and then further supervised learning algorithms are used for their classification. Here, we have extracted texture features along with statistical features and supplied various extracted features to classifiers. We have used seven different classifiers known as k-nearest neighbors classifier, support vector machine classifier, decision tree classifier, multinomial naive Bayes classifier, stochastic gradient descent classifier, random forest classifier, and multi-layer perceptron (MLP) classifier. We have used dataset of 15750 clinical images consisting of both 6910 benign and 8840 malignant lung cancer related images to train and test these classifiers. In the obtained results, it is found that accuracy of MLP classifier is higher with value of 88.55% in comparison with the other classifiers.

Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Recognition and prediction of lung cancer in the earliest reference point stage can be very useful to improve the survival rate of patients. But diagnosis of cancer is one the major challenging task for radiologist. For detecting, predicting and diagnosing lung cancer, an intelligent computer-aided diagnosis system can be very much useful for radiologist. This paper proposed an efficient lung cancer detection and prediction algorithm using multi-class SVM (Support Vector Machine) classifier. Multi-stage classification was used for the detection of cancer. This system can also predict the probability of lung cancer. In every stage of classification image enhancement and segmentation have been done separately. Image scaling, color space transformation and contrast enhancement have been used for image enhancement. Threshold and marker-controlled watershed based segmentation has been used for segmentation. For classification purpose, SVM binary classifier was used. Our proposed technique shows higher degree of accuracy in lung cancer detection and prediction

Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifie

Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer can increase the chance of survival among people. The overall 5-year survival rate for lung cancer patients increases from 14 to 49% if the disease is detected in time. Although Computed Tomography (CT) can be more efficient than X-ray. However, problem seemed to merge due to time constraint in detecting the present of lung cancer regarding on the several diagnosing method used. Hence, a lung cancer detection system using image processing is used to classify the present of lung cancer in an CT-images. In this study, MATLAB have been used through every procedures made. In image processing procedures, process such as image pre-processing, segmentation and feature extraction have been discussed in detail. We are aiming to get the more accurate results by using various enhancement and segmentation techniques.

Lung Cancer Detection on CT Images by Using Image Processing

With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and labor intensive. This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology. Information Extraction systems takes natural language text as input and produces structured information specified by certain criteria, that is relevant to a particular application. Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural Language Processing (NLP) tasks such as Machine Translation, Question-Answering System, Natural Language Understanding, Text Summarization and Digital Assistants like Siri, Cortana and Google Now. This paper introduces Information Extraction technology, its various sub-tasks, highlights state-of-the-art research in various IE subtasks, current challenges and future research directions.

Natural Language Processing for Information Extraction.

Interpreting medical images and summarising them in the form of radiology reports is a challenging, tedious, and complex task. A radiologist provides a complete description of a medical image in the form of radiology report by describing normal or abnormal findings and providing a summary for decision making. Research shows that the radiology practice is error-prone due to the limited number of experts, increasing patient volumes, and the subjective nature of human perception. To reduce the number of diagnostic errors and to alleviate the task of radiologists, there is a need for a computer-aided report generation system that can automatically generate a radiology report for a given medical image. We propose an encoder-decoder based framework that can automatically generate radiology reports from medical images. Specifically, we use a Convolutional Neural Network as an encoder coupled with a multi-stage Stacked Long Short-Term Memory as a decoder to generate reports. We perform experiments on the Indiana University Chest X-ray collection, a publicly available dataset, to measure the effectiveness of our model. Experimental results show the effectiveness of our model in automatically generating radiology reports from medical images.

From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach

Medical image classification and concept detection are two important tasks for efficient and robust medical retrieval systems and also help with downstream tasks such as knowledge discovery, medical report generation, medical question answering, and clinical decision making. We investigate the effectiveness of transfer learning on the modality classification task using state-of-the-art deep convolutional neural networks pretrained on generic images. We also compare the performance of the traditional pipeline of handcrafted features with multi-label learning algorithms with end-to-end deep learning features for the concept detection task. Experimental results on the modality classification task show that transfer learning can leverage the patterns learned from large training data to the medical domain where little labeled data is available. Moreover, results on the concept detection task show that the deep learning approach provides better and more powerful feature representations compared to handcrafted feature extraction methods. The results on both tasks suggest that deep transfer learning methods are effective in the medical domain where data is scarce.

Modality Classification and Concept Detection in Medical Images Using Deep Transfer Learning

Video Surveillance System is a powerful tool used for monitoring people and their activities for public security. The motive of having surveillance system is not only to put cameras in place of human eyes, but also making it capable for recognizing activities automatically. In this paper, human detection and tracking is performed on Weizmann dataset having various activities like run, bend, hand wave, skip, etc. First background modelling is done by taking mean of first n frames. After this, human detection is done using background subtraction algorithm and then tracking is done using Kalman filter. Result of each stage has been discussed. The proposed methodology shows promising results which can further be used for activity recognition.

Sonit Singh

Papers

Lung Cancer Detection on CT Images by Using Image Processing

Natural Language Processing for Information Extraction.

From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach

Modality Classification and Concept Detection in Medical Images Using Deep Transfer Learning

Background modelling, detection and tracking of human in video surveillance system