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Deep Learning Models for Digital Pathology [article]

Aïcha BenTaieb, Ghassan Hamarneh
2019 arXiv   pre-print
Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine. However, diagnostics from histopathology images generally rely on a visual cognitive assessment of tissue slides which implies an inherent element of interpretation and hence subjectivity. Access to digitized histopathology images enabled the development of computational systems aiming at reducing manual
more » ... ntion and automating parts of pathologists' workflow. Specifically, applications of deep learning to histopathology image analysis now offer opportunities for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However digitized histopathology tissue slides are unique in a variety of ways and come with their own set of computational challenges. In this survey, we summarize the different challenges facing computational systems for digital pathology and provide a review of state-of-the-art works that developed deep learning-based solutions for the predictive modeling of histopathology images from a detection, stain normalization, segmentation, and tissue classification perspective. We then discuss the challenges facing the validation and integration of such deep learning-based computational systems in clinical workflow and reflect on future opportunities for histopathology derived image measurements and better predictive modeling.
arXiv:1910.12329v2 fatcat:2b7h7i2zwbautewneabghm3bzi

Topology Aware Fully Convolutional Networks for Histology Gland Segmentation [chapter]

Aïcha BenTaieb, Ghassan Hamarneh
2016 Lecture Notes in Computer Science  
Experimental Performance Evaluation Proposed method vs. various architectures Proposed method vs. graphical models +10 to 15% pixel accuracy +3 to 5% Dice -2 to +3% pixel accuracy +13 to 38% Dice
doi:10.1007/978-3-319-46723-8_53 fatcat:a6uglpvlhndnjiyuky5xpxfpey

Automatic Diagnosis of Ovarian Carcinomas via Sparse Multiresolution Tissue Representation [chapter]

Aïcha BenTaieb, Hector Li-Chang, David Huntsman, Ghassan Hamarneh
2015 Lecture Notes in Computer Science  
It has now been convincingly demonstrated that ovarian carcinoma subtypes are not a single disease but comprise a heterogeneous group of neoplasms. Whole slide images of tissue sections are used clinically for diagnosing biologically distinct subtypes, as opposed to different grades of the same disease. This new grading scheme for ovarian carcinomas results in a low to moderate interobserver agreement among pathologists. In practice, the majority of cases are diagnosed at advanced stages and
more » ... overall prognosis is typically poor. In this work, we propose an automatic system for the diagnosis of ovarian carcinoma subtypes from large-scale histopathology images. Our novel approach uses an unsupervised feature learning framework composed of a sparse tissue representation and a discriminative feature encoding scheme. We validate our model on a challenging clinical dataset of 80 patients and demonstrate its ability to diagnose whole slide images with an average accuracy of 91% using a linear support vector machine classifier.
doi:10.1007/978-3-319-24553-9_77 fatcat:27nyvav375eanbfrjg3sgeymqm

Deep Learning Based Image Reconstruction for Diffuse Optical Tomography [chapter]

Hanene Ben Yedder, Aïcha BenTaieb, Majid Shokoufi, Amir Zahiremami, Farid Golnaraghi, Ghassan Hamarneh
2018 Lecture Notes in Computer Science  
Diffuse optical tomography (DOT) is a relatively new imaging modality that has demonstrated its clinical potential of probing tumors in a non-invasive and affordable way. Image reconstruction is an ill-posed challenging task because knowledge of the exact analytic inverse transform does not exist a priori, especially in the presence of sensor non-idealities and noise. Standard reconstruction approaches involve approximating the inverse function and often require expert parameters tuning to
more » ... ize reconstruction performance. In this work, we evaluate the use of a deep learning model to reconstruct images directly from their corresponding DOT projection data. The inverse problem is solved by training the model via training pairs created using physics-based simulation. Both quantitative and qualitative results indicate the superiority of the proposed network compared to an analytic technique.
doi:10.1007/978-3-030-00129-2_13 fatcat:znuyz24yafborhjwbkn5we7xhi

Select, Attend, and Transfer: Light, Learnable Skip Connections [article]

Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh
2018 arXiv   pre-print
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients. They equip encoder-decoder-like networks with richer feature representations, but at the cost of higher memory usage, computation, and possibly resulting in transferring non-discriminative feature maps. In this paper, we focus on improving skip connections used in segmentation networks
more » ... U-Net, V-Net, and The One Hundred Layers Tiramisu (DensNet) architectures). We propose light, learnable skip connections which learn to first select the most discriminative channels and then attend to the most discriminative regions of the selected feature maps. The output of the proposed skip connections is a unique feature map which not only reduces the memory usage and network parameters to a high extent, but also improves segmentation accuracy. We evaluate the proposed method on three different 2D and volumetric datasets and demonstrate that the proposed light, learnable skip connections can outperform the traditional heavy skip connections in terms of segmentation accuracy, memory usage, and number of network parameters.
arXiv:1804.05181v3 fatcat:mkah3fhafbfabnoayqt66cq64i

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear [chapter]

Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant
2018 Lecture Notes in Computer Science  
BenTaieb*; Ghassan Hamarneh M-122 A Deep Model with Shape-preserving Loss for Gland Instance Segmentation Zengqiang Yan*; Xin Yang; Kwang-Ting Cheng M-123 Model-based Refinement of Nonlinear Registrations  ...  Followed Network Model for Retinal Vessels Segmentation Wu YiCheng; Yong Xia*; Yang Song; Yanning Zhang; Weidong Cai M-121 Predicting Cancer with a Recurrent Visual Attention Model for Histopathology Images Aicha  ... 
doi:10.1007/978-3-030-00928-1_1 fatcat:ypoj3zplm5awljf6u5c2spgiea