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"cancer detection": models, code, and papers

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Jul 23, 2021
Abhir Bhandary, Ananth Prabhu G, Mustafa Basthikodi, Chaitra K M

Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we proposed a novel methodology for lung Segmentation on the basis of Fuzzy C-Means Clustering, Adaptive Thresholding, and Segmentation of Active Contour Model. The experimental results are analysed and presented.

* International Journal of Engineering Trends and Technology 69.5(2021):85-93 
* 9 pages, 10 figures, Published with International Journal of Engineering Trends and Technology (IJETT) 
  
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RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection

May 30, 2018
Tian Lan, Yuanyuan Li, Jonah Kimani Murugi, Yi Ding, Zhiguang Qin

The early detection and early diagnosis of lung cancer are crucial to improve the survival rate of lung cancer patients. Pulmonary nodules detection results have a significant impact on the later diagnosis. In this work, we propose a new network named RUN to complete nodule detection in a single step by bypassing the candidate selection. The system introduces the shortcut of the residual network to improve the traditional U-Net, thereby solving the disadvantage of poor results due to its lack of depth. Furthermore, we compare the experimental results with the traditional U-Net. We validate our method in LUng Nodule Analysis 2016 (LUNA16) Nodule Detection Challenge. We acquire a sensitivity of 90.90% at 2 false positives per scan and therefore achieve better performance than the current state-of-the-art approaches.

* 15 pages, 5 figures, manuscript for Neurocomputing 
  
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Diagnosis of Breast Cancer using Hybrid Transfer Learning

Mar 23, 2020
Subrato Bharati, Prajoy Podder

Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.

* 24 pages, 11 figures 
  
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Discriminative Localized Sparse Representations for Breast Cancer Screening

Nov 20, 2020
Sokratis Makrogiannis, Chelsea E. Harris, Keni Zheng

Breast cancer is the most common cancer among women both in developed and developing countries. Early detection and diagnosis of breast cancer may reduce its mortality and improve the quality of life. Computer-aided detection (CADx) and computer-aided diagnosis (CAD) techniques have shown promise for reducing the burden of human expert reading and improve the accuracy and reproducibility of results. Sparse analysis techniques have produced relevant results for representing and recognizing imaging patterns. In this work we propose a method for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA). In this work we apply dictionary learning to our block based sparse analysis method to classify breast lesions as benign or malignant. The performance of our method in conjunction with LC-KSVD dictionary learning is evaluated using 10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results indicate that the proposed sparse analyses may be a useful component for breast cancer screening applications.

  
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Bridging the gap between prostate radiology and pathology through machine learning

Dec 03, 2021
Indrani Bhattacharya, David S. Lim, Han Lin Aung, Xingchen Liu, Arun Seetharaman, Christian A. Kunder, Wei Shao, Simon J. C. Soerensen, Richard E. Fan, Pejman Ghanouni, Katherine J. To'o, James D. Brooks, Geoffrey A. Sonn, Mirabela Rusu

Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we compare different labeling strategies, namely, pathology-confirmed radiologist labels, pathologist labels on whole-mount histopathology images, and lesion-level and pixel-level digital pathologist labels (previously validated deep learning algorithm on histopathology images to predict pixel-level Gleason patterns) on whole-mount histopathology images. We analyse the effects these labels have on the performance of the trained machine learning models. Our experiments show that (1) radiologist labels and models trained with them can miss cancers, or underestimate cancer extent, (2) digital pathologist labels and models trained with them have high concordance with pathologist labels, and (3) models trained with digital pathologist labels achieve the best performance in prostate cancer detection in two different cohorts with different disease distributions, irrespective of the model architecture used. Digital pathologist labels can reduce challenges associated with human annotations, including labor, time, inter- and intra-reader variability, and can help bridge the gap between prostate radiology and pathology by enabling the training of reliable machine learning models to detect and localize prostate cancer on MRI.

* Indrani Bhattacharya and David S. Lim contributed equally as first authors. Geoffrey A. Sonn and Mirabela Rusu contributed equally as senior authors 
  
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Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

Nov 22, 2017
Fangzhou Liao, Ming Liang, Zhe Li, Xiaolin Hu, Sen Song

Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3D deep neural network to solve this problem. The model consists of two modules. The first one is a 3D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities and combines them with a leaky noisy-or gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The over-fitting caused by the shortage of training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition. The code has been made publicly available.

* 12 pages, 9 figures 
  
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CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

Dec 02, 2021
Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith

Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screening-time detection is commonly caused by the tumor being obscured by its surrounding breast tissues, a phenomenon called masking. To study and benchmark mammographic masking of cancer, in this work we introduce CSAW-M, the largest public mammographic dataset, collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We also trained deep learning models on CSAW-M to estimate the masking level and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers -- without being explicitly trained for these tasks -- than its breast density counterparts.

* 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks 
  
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Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer

Sep 18, 2019
Mohammed Zubair M. Shamim, Sadatullah Syed, Mohammad Shiblee, Mohammed Usman, Syed Ali

Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of specialists in such areas. Automating the initial screening process using artificial intelligence (AI) to detect pre-cancerous lesions can prove to be an effective and inexpensive technique that would allow patients to be triaged accordingly to receive appropriate clinical management. In this study, we have applied and evaluated the efficacy of six deep convolutional neural network (DCNN) models using transfer learning, for identifying pre-cancerous tongue lesions directly using a small data set of clinically annotated photographic images to diagnose early signs of OCC. DCNN model based on Vgg19 architecture was able to differentiate between benign and pre-cancerous tongue lesions with a mean classification accuracy of 0.98, sensitivity 0.89 and specificity 0.97. Additionally, the ResNet50 DCNN model was able to distinguish between five types of tongue lesions i.e. hairy tongue, fissured tongue, geographic tongue, strawberry tongue and oral hairy leukoplakia with a mean classification accuracy of 0.97. Preliminary results using an (AI+Physician) ensemble model demonstrate that an automated initial screening process of tongue lesions using DCNNs can achieve near-human level classification performance for diagnosing early signs of OCC in patients.

* 25 pages, 10 figures 
  
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Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation

Dec 30, 2020
Debesh Jha, Steven A. Hicks, Krister Emanuelsen, Håvard Johansen, Dag Johansen, Thomas de Lange, Michael A. Riegler, Pål Halvorsen

Colorectal cancer is the third most common cause of cancer worldwide. According to Global cancer statistics 2018, the incidence of colorectal cancer is increasing in both developing and developed countries. Early detection of colon anomalies such as polyps is important for cancer prevention, and automatic polyp segmentation can play a crucial role for this. Regardless of the recent advancement in early detection and treatment options, the estimated polyp miss rate is still around 20\%. Support via an automated computer-aided diagnosis system could be one of the potential solutions for the overlooked polyps. Such detection systems can help low-cost design solutions and save doctors time, which they could for example use to perform more patient examinations. In this paper, we introduce the 2020 Medico challenge, provide some information on related work and the dataset, describe the task and evaluation metrics, and discuss the necessity of organizing the Medico challenge.

* MediaEval 2020 
  
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Recent advances in deep learning applied to skin cancer detection

Dec 06, 2019
Andre G. C. Pacheco, Renato A. Krohling

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals and medical instruments are significant issues in this field. In this context, over the past few years, deep learning models applied to automated skin cancer detection have become a trend. In this paper, we present an overview of the recent advances reported in this field as well as a discussion about the challenges and opportunities for improvement in the current models. In addition, we also present some important aspects regarding the use of these models in smartphones and indicate future directions we believe the field will take.

* Paper accepted in the Retrospectives Workshop @ NeurIPS 2019 
  
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