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"cancer detection": models, code, and papers

Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images

Dec 05, 2016
Pegah Faridi, Habibollah Danyali, Mohammad Sadegh Helfroush, Mojgan Akbarzadeh Jahromi

Early detection and prognosis of breast cancer are feasible by utilizing histopathological grading of biopsy specimens. This research is focused on detection and grading of nuclear pleomorphism in histopathological images of breast cancer. The proposed method consists of three internal steps. First, unmixing colors of H&E is used in the preprocessing step. Second, nuclei boundaries are extracted incorporating the center of cancerous nuclei which are detected by applying morphological operations and Difference of Gaussian filter on the preprocessed image. Finally, segmented nuclei are scored to accomplish one parameter of the Nottingham grading system for breast cancer. In this approach, the nuclei area, chromatin density, contour regularity, and nucleoli presence, are features for nuclear pleomorphism scoring. Experimental results showed that the proposed algorithm, with an accuracy of 86.6%, made significant advancement in detecting cancerous nuclei compared to existing methods in the related literature.

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Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection

Oct 20, 2015
Audrey G. Chung, Mohammad Javad Shafiee, Devinder Kumar, Farzad Khalvati, Masoom A. Haider, Alexander Wong

Prostate cancer is the most diagnosed form of cancer in Canadian men, and is the third leading cause of cancer death. Despite these statistics, prognosis is relatively good with a sufficiently early diagnosis, making fast and reliable prostate cancer detection crucial. As imaging-based prostate cancer screening, such as magnetic resonance imaging (MRI), requires an experienced medical professional to extensively review the data and perform a diagnosis, radiomics-driven methods help streamline the process and has the potential to significantly improve diagnostic accuracy and efficiency, and thus improving patient survival rates. These radiomics-driven methods currently rely on hand-crafted sets of quantitative imaging-based features, which are selected manually and can limit their ability to fully characterize unique prostate cancer tumour phenotype. In this study, we propose a novel \textit{discovery radiomics} framework for generating custom radiomic sequences tailored for prostate cancer detection. Discovery radiomics aims to uncover abstract imaging-based features that capture highly unique tumour traits and characteristics beyond what can be captured using predefined feature models. In this paper, we discover new custom radiomic sequencers for generating new prostate radiomic sequences using multi-parametric MRI data. We evaluated the performance of the discovered radiomic sequencer against a state-of-the-art hand-crafted radiomic sequencer for computer-aided prostate cancer detection with a feedforward neural network using real clinical prostate multi-parametric MRI data. Results for the discovered radiomic sequencer demonstrate good performance in prostate cancer detection and clinical decision support relative to the hand-crafted radiomic sequencer. The use of discovery radiomics shows potential for more efficient and reliable automatic prostate cancer detection.

* 8 pages 

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Prostate Cancer Detection using Deep Convolutional Neural Networks

May 30, 2019
Sunghwan Yoo, Isha Gujrathi, Masoom A. Haider, Farzad Khalvati

Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNNs architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNNs-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 healthy patients. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95% Confidence Interval (CI): 0.84-0.90) and 0.84 (95% CI: 0.76-0.91) at slice level and patient level, respectively.

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Method and System for Image Analysis to Detect Cancer

Aug 26, 2019
Waleed A. Yousef, Ahmed A. Abouelkahire, Deyaaeldeen Almahallawi, Omar S. Marzouk, Sameh K. Mohamed, Waleed A. Mustafa, Omar M. Osama, Ali A. Saleh, Naglaa M. Abdelrazek

Breast cancer is the most common cancer and is the leading cause of cancer death among women worldwide. Detection of breast cancer, while it is still small and confined to the breast, provides the best chance of effective treatment. Computer Aided Detection (CAD) systems that detect cancer from mammograms will help in reducing the human errors that lead to missing breast carcinoma. Literature is rich of scientific papers for methods of CAD design, yet with no complete system architecture to deploy those methods. On the other hand, commercial CADs are developed and deployed only to vendors' mammography machines with no availability to public access. This paper presents a complete CAD; it is complete since it combines, on a hand, the rigor of algorithm design and assessment (method), and, on the other hand, the implementation and deployment of a system architecture for public accessibility (system). (1) We develop a novel algorithm for image enhancement so that mammograms acquired from any digital mammography machine look qualitatively of the same clarity to radiologists' inspection; and is quantitatively standardized for the detection algorithms. (2) We develop novel algorithms for masses and microcalcifications detection with accuracy superior to both literature results and the majority of approved commercial systems. (3) We design, implement, and deploy a system architecture that is computationally effective to allow for deploying these algorithms to cloud for public access.

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A Method to Facilitate Cancer Detection and Type Classification from Gene Expression Data using a Deep Autoencoder and Neural Network

Dec 20, 2018
Xi Chen, Jin Xie, Qingcong Yuan

With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensionality and complexity of the data. Here we present models of deep learning (DL) and apply them to gene expression data for the diagnosis and categorization of cancer. In this study, we have developed two DL models using messenger ribonucleic acid (mRNA) datasets available from the Genomic Data Commons repository. Our models achieved 98% accuracy in cancer detection, with false negative and false positive rates below 1.7%. In our results, we demonstrated that 18 out of 32 cancer-typing classifications achieved more than 90% accuracy. Due to the limitation of a small sample size (less than 50 observations), certain cancers could not achieve a higher accuracy in typing classification, but still achieved high accuracy for the cancer detection task. To validate our models, we compared them with traditional statistical models. The main advantage of our models over traditional cancer detection is the ability to use data from various cancer types to automatically form features to enhance the detection and diagnosis of a specific cancer type.

* 6 pages 

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Transfer Learning for Oral Cancer Detection using Microscopic Images

Nov 23, 2020
Rutwik Palaskar, Renu Vyas, Vilas Khedekar, Sangeeta Palaskar, Pranjal Sahu

Oral cancer has more than 83% survival rate if detected in its early stages, however, only 29% of cases are currently detected early. Deep learning techniques can detect patterns of oral cancer cells and can aid in its early detection. In this work, we present the first results of neural networks for oral cancer detection using microscopic images. We compare numerous state-of-the-art models via transfer learning approach and collect and release an augmented dataset of high-quality microscopic images of oral cancer. We present a comprehensive study of different models and report their performance on this type of data. Overall, we obtain a 10-15% absolute improvement with transfer learning methods compared to a simple Convolutional Neural Network baseline. Ablation studies show the added benefit of data augmentation techniques with finetuning for this task.

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Lung Cancer Detection and Classification based on Image Processing and Statistical Learning

Nov 25, 2019
Md Rashidul Hasan, Muntasir Al Kabir

Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer.Using a data set of thousands of high-resolution lung scans collected from Kaggle competition [1], we will develop algorithms that accurately determine in the lungs are cancerous or not. The proposed system promises better result than the existing systems, which would be beneficial for the radiologist for the accurate and early detection of cancer. The method has been tested on 198 slices of CT images of various stages of cancer obtained from Kaggle dataset[1] and is found satisfactory results. The accuracy of the proposed method in this dataset is 72.2%

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Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection

Mar 05, 2021
Ahmed Rasheed, Muhammad Shahzad Younis, Junaid Qadir, Muhammad Bilal

Breast cancer is one of the most common cause of deaths among women. Mammography is a widely used imaging modality that can be used for cancer detection in its early stages. Deep learning is widely used for the detection of cancerous masses in the images obtained via mammography. The need to improve accuracy remains constant due to the sensitive nature of the datasets so we introduce segmentation and wavelet transform to enhance the important features in the image scans. Our proposed system aids the radiologist in the screening phase of cancer detection by using a combination of segmentation and wavelet transforms as pre-processing augmentation that leads to transfer learning in neural networks. The proposed system with these pre-processing techniques significantly increases the accuracy of detection on Mini-MIAS.

* 9 pages 

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Ensemble classifier approach in breast cancer detection and malignancy grading- A review

Apr 11, 2017
Deepti Ameta

The diagnosed cases of Breast cancer is increasing annually and unfortunately getting converted into a high mortality rate. Cancer, at the early stages, is hard to detect because the malicious cells show similar properties (density) as shown by the non-malicious cells. The mortality ratio could have been minimized if the breast cancer could have been detected in its early stages. But the current systems have not been able to achieve a fully automatic system which is not just capable of detecting the breast cancer but also can detect the stage of it. Estimation of malignancy grading is important in diagnosing the degree of growth of malicious cells as well as in selecting a proper therapy for the patient. Therefore, a complete and efficient clinical decision support system is proposed which is capable of achieving breast cancer malignancy grading scheme very efficiently. The system is based on Image processing and machine learning domains. Classification Imbalance problem, a machine learning problem, occurs when instances of one class is much higher than the instances of the other class resulting in an inefficient classification of samples and hence a bad decision support system. Therefore EUSBoost, ensemble based classifier is proposed which is efficient and is able to outperform other classifiers as it takes the benefits of both-boosting algorithm with Random Undersampling techniques. Also comparison of EUSBoost with other techniques is shown in the paper.

* International Journal of Managing Public Sector Information and Communication Technologies (IJMPICT) Vol. 8, No. 1, March 2017 
* 10 pages,1 figure,5 tables 

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A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios

May 22, 2020
Apostolia Tsirikoglou, Karin Stacke, Gabriel Eilertsen, Martin Lindvall, Jonas Unger

Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) methods for classification and detection have shown great potential, but often require large amounts of training data that are hard to collect, and annotate. For many cancer types, the scarceness of data creates barriers for training DL models. One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming. DL-based tools can allow faster diagnosis, with potentially increased quality. Unfortunately, due to the sparsity of tumor cells, annotating this type of data demands a high level of effort from pathologists. Using weak annotations from slide-level images have shown great potential, but demand access to a substantial amount of data as well. In this study, we investigate mitigation strategies for limited data access scenarios. Particularly, we address whether it is possible to exploit mutual structure between tissues to develop general techniques, wherein data from one type of cancer in a particular tissue could have diagnostic value for other cancers in other tissues. Our case is exemplified by a DL model for metastatic colon cancer detection in lymph nodes. Could such a model be trained with little or even no lymph node data? As alternative data sources, we investigate 1) tumor cells taken from the primary colon tumor tissue, and 2) cancer data from a different organ (breast), either as is or transformed to the target domain (colon) using Cycle-GANs. We show that the suggested approaches make it possible to detect cancer metastasis with no or very little lymph node data, opening up for the possibility that existing, annotated histopathology data could generalize to other domains.

* Presented at the ICLR 2020 Workshop on AI for Overcoming Global Disparities in Cancer Care (AI4CC) 

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Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

Mar 01, 2020
Li Xiao, Cheng Zhu, Junjun Liu, Chunlong Luo, Peifang Liu, Yi Zhao

Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classes and their bounding boxes. On the other hand, medical images such as mammography usually contain normal regions or objects that are similar to the lesion region, and may be misclassified in the testing stage if they are not taken care of. In this paper, we address such problem by introducing a novel top likelihood loss together with a new sampling procedure to select and train the suspected target regions, as well as proposing a similarity loss to further identify suspected targets from targets. Mean average precision (mAP) according to the predicted targets and specificity, sensitivity, accuracy, AUC values according to classification of patients are adopted for performance comparisons. We firstly test our proposed method on a private dense mammogram dataset. Results show that our proposed method greatly reduce the false positive rate and the specificity is increased by 0.25 on detecting mass type cancer. It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists. Our method is also validated on the public Digital Database for Screening Mammography (DDSM) dataset, brings significant improvement on mass type cancer detection and outperforms the most state-of-the-art work.

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CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of Skin Cancer from Dermoscopy Images

Nov 21, 2020
James Ren Hou Lee, Maya Pavlova, Mahmoud Famouri, Alexander Wong

Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective skin cancer detection due to strong prognosis when treated at an early stage, with one of the key screening approaches being dermoscopy examination. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public as part of the Cancer-Net initiative. To the best of the authors' knowledge, CancerNet-SCa comprises of the first machine-designed deep neural network architecture designs tailored specifically for skin cancer detection, one of which possessing a self-attention architecture design with attention condensers. Furthermore, we investigate and audit the behaviour of CancerNet-SCa in a responsible and transparent manner via explainability-driven model auditing. While CancerNet-SCa is not a production-ready screening solution, the hope is that the release of CancerNet-SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

* 8 pages 

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Microwave breast cancer detection using Empirical Mode Decomposition features

Feb 24, 2017
Hongchao Song, Yunpeng Li, Mark Coates, Aidong Men

Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detecting the existence of breast tumours rather than performing imaging to identify the exact tumour position. A key step of the machine learning approaches is feature extraction. One of the most widely used feature extraction method is principle component analysis (PCA). However, it can be sensitive to signal misalignment. This paper presents an empirical mode decomposition (EMD)-based feature extraction method, which is more robust to the misalignment. Experimental results involving clinical data sets combined with numerically simulated tumour responses show that combined features from EMD and PCA improve the detection performance with an ensemble selection-based classifier.

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Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

Feb 21, 2019
Jiachen Wang, Riqiang Gao, Yuankai Huo, Shunxing Bao, Yunxi Xiong, Sanja L. Antic, Travis J. Osterman, Pierre P. Massion, Bennett A. Landman

Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. However, this strategy yields high false positive rates, which can lead to unnecessary and potentially harmful procedures. To address such challenges, we established a pipeline that co-learns from detailed clinical demographics and 3D CT images. Toward this end, we leveraged data from the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), which focuses on early detection of lung cancer. A 3D attention-based deep convolutional neural net (DCNN) is proposed to identify lung cancer from the chest CT scan without prior anatomical location of the suspicious nodule. To improve upon the non-invasive discrimination between benign and malignant, we applied a random forest classifier to a dataset integrating clinical information to imaging data. The results show that the AUC obtained from clinical demographics alone was 0.635 while the attention network alone reached an accuracy of 0.687. In contrast when applying our proposed pipeline integrating clinical and imaging variables, we reached an AUC of 0.787 on the testing dataset. The proposed network both efficiently captures anatomical information for classification and also generates attention maps that explain the features that drive performance.

* SPIE Medical Image, oral presentation 

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A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data

Jan 17, 2019
Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin, George W. Sledge Jr., Nigam H. Shah, Suzanne R. Tamang

Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we applied a semi-supervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Materials and Methods: We studied 11,459 female patients treated at Stanford Health Care who received an incident breast cancer diagnosis from 2000-2014. The dataset consisted of structured data and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results (SEER) database. We extracted information on metastatic disease from patient notes to infer a class label and then trained a regularized logistic regression model for MBC classification. We evaluated model performance on a gold standard set of set of 146 patients. Results: There are 495 patients with de novo stage IV MBC, 1,374 patients initially diagnosed with Stage 0-III disease had recurrent MBC, and 9,590 had no evidence of metastatis. The median follow-up time is 96.3 months (mean 97.8, standard deviation 46.7). The best-performing model incorporated both EMR and CCR features. The area under the receiver-operating characteristic curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861, specificity=0.878 and overall accuracy=0.870. Discussion and Conclusion: A framework for MBC case detection combining EMR and CCR data achieved good sensitivity, specificity and discrimination without requiring expert-labeled examples. This approach enables population-based research on how patients die from cancer and may identify novel predictors of cancer recurrence.

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The impact of patient clinical information on automated skin cancer detection

Sep 16, 2019
Andre G. C. Pacheco, Renato A. Krohling

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.

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Ensembles of Radial Basis Function Networks for Spectroscopic Detection of Cervical Pre-Cancer

May 20, 1999
Kagan Tumer, Nirmala Ramanujam, Joydeep Ghosh, Rebecca Richards-Kortum

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, non-invasively and quantitatively probes the biochemical and morphological changes that occur in pre-cancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337, 380 and 460 nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from pre-cancerous tissue samples. The use of connectionist methods such as multi layered perceptrons, radial basis function networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated, and near real-time implementation of pre-cancer detection in the hands of non-experts. The results are more reliable, direct and accurate than those achieved by either human experts or multivariate statistical algorithms.

* IEEE Transactions on Biomedical Engineering, vol 45, no. 8, pp 953-962, 1998 
* 23 pages 

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Proposing method to Increase the detection accuracy of stomach cancer based on colour and lint features of tongue using CNN and SVM

Nov 18, 2020
Elham Gholami, Seyed Reza Kamel Tabbakh, Maryam Kheirabadi

Today, gastric cancer is one of the diseases which affected many people's life. Early detection and accuracy are the main and crucial challenges in finding this kind of cancer. In this paper, a method to increase the accuracy of the diagnosis of detecting cancer using lint and colour features of tongue based on deep convolutional neural networks and support vector machine is proposed. In the proposed method, the region of tongue is first separated from the face image by {deep RCNN} \color{black} Recursive Convolutional Neural Network (R-CNN) \color{black}. After the necessary preprocessing, the images to the convolutional neural network are provided and the training and test operations are triggered. The results show that the proposed method is correctly able to identify the area of the tongue as well as the patient's person from the non-patient. Based on experiments, the DenseNet network has the highest accuracy compared to other deep architectures. The experimental results show that the accuracy of this network for gastric cancer detection reaches 91% which shows the superiority of method in comparison to the state-of-the-art methods.

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Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection

Apr 21, 2020
Marcel Bengs, Stephan Westermann, Nils Gessert, Dennis Eggert, Andreas O. H. Gerstner, Nina A. Mueller, Christian Betz, Wiebke Laffers, Alexander Schlaefer

Early detection of head and neck tumors is crucial for patient survival. Often, diagnoses are made based on endoscopic examination of the larynx followed by biopsy and histological analysis, leading to a high inter-observer variability due to subjective assessment. In this regard, early non-invasive diagnostics independent of the clinician would be a valuable tool. A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tissue. However, HSI data processing is challenging due to high spectral variations, various image interferences, and the high dimensionality of the data. Therefore, performance of automatic HSI analysis has been limited and so far, mostly ex-vivo studies have been presented with deep learning. In this work, we analyze deep learning techniques for in-vivo hyperspectral laryngeal cancer detection. For this purpose we design and evaluate convolutional neural networks (CNNs) with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. For evaluation, we use an in-vivo data set with HSI of the oral cavity or oropharynx. Overall, we present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI and we show that jointly learning from the spatial and spectral domain improves classification accuracy notably. Our 3D spatio-spectral Densenet achieves an average accuracy of 81%.

* Accepted at SPIE Medical Imaging 2020 

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Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection

Apr 07, 2021
Zhizhong Chai, Luyang Luo, Huangjing Lin, Hao Chen, Pheng-Ann Heng

With availability of huge amounts of labeled data, deep learning has achieved unprecedented success in various object detection tasks. However, large-scale annotations for medical images are extremely challenging to be acquired due to the high demand of labour and expertise. To address this difficult issue, in this paper we propose a novel semi-supervised deep metric learning method to effectively leverage both labeled and unlabeled data with application to cervical cancer cell detection. Different from previous methods, our model learns an embedding metric space and conducts dual alignment of semantic features on both the proposal and prototype levels. First, on the proposal level, we generate pseudo labels for the unlabeled data to align the proposal features with learnable class proxies derived from the labeled data. Furthermore, we align the prototypes generated from each mini-batch of labeled and unlabeled data to alleviate the influence of possibly noisy pseudo labels. Moreover, we adopt a memory bank to store the labeled prototypes and hence significantly enrich the metric learning information from larger batches. To comprehensively validate the method, we construct a large-scale dataset for semi-supervised cervical cancer cell detection for the first time, consisting of 240,860 cervical cell images in total. Extensive experiments show our proposed method outperforms other state-of-the-art semi-supervised approaches consistently, demonstrating efficacy of deep semi-supervised metric learning with dual alignment on improving cervical cancer cell detection performance.

* 11 pages 

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