Feasibility of fully automated classification of whole slide images based on deep learning

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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P3-014: DEEP LEARNING IN AUTOMATED CLASSIFICATION OF ADVERSE EVENTS IN CLINICAL STUDIES OF ALZHEIMER'S DISEASE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.1369 ◽

2006 ◽

Vol 14 (7S_Part_19) ◽

pp. P1067-P1068

Author(s):

Pradeep Anand Ravindranath ◽

Rema Raman ◽

Tiffany W. Chow ◽

Michael S. Rafii ◽

Paul S. Aisen ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Adverse Events ◽

Clinical Studies ◽

Automated Classification

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Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

Journal of Biophotonics ◽

10.1002/jbio.201800435 ◽

2019 ◽

Vol 12 (7) ◽

Cited By ~ 6

Author(s):

Hongxin Lin ◽

Chao Wei ◽

Guangxing Wang ◽

Hu Chen ◽

Lisheng Lin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Multiphoton Microscopy ◽

Automated Classification

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Automated Classification of Diabetic Retinopathy Severity: A Deep Learning Approach

10.5339/qfarc.2018.hbpd1007 ◽

2018 ◽

Author(s):

Rawan AlSaad ◽

Sabri Boughorbel ◽

Somaya Al-Maadeed

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Learning Approach ◽

Automated Classification

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A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

10.2337/figshare.14710284.v1 ◽

2021 ◽

Author(s):

Fangyao Tang ◽

Xi Wang ◽

An-ran Ran ◽

Carmen KM Chan ◽

Mary Ho ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Macular Edema ◽

Diabetic Macular Edema ◽

Vision Loss ◽

Three Dimensional ◽

Learning System ◽

Optical Coherence ◽

Automated Classification

<a>Objective:</a> Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. Research Design and Methods: We trained and validated two versions of a multi-task convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D CNNs, we employed the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia. Results: In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary dataset and greater than 0.894 for the external datasets. Conclusion: We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.

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A pyramidal deep learning pipeline for kidney whole-slide histology images classification

Scientific Reports ◽

10.1038/s41598-021-99735-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hisham Abdeltawab ◽

Fahmi Khalifa ◽

Mohammed Mohammed ◽

Liang Cheng ◽

Dibson Gondim ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Correct Diagnosis ◽

Papillary Renal Cell Carcinoma ◽

Automated Classification ◽

Cell Renal Cell Carcinoma ◽

Whole Slide Images

AbstractRenal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist’s experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist’s capabilities by providing automated classification for histopathological images.

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Automated classification of brain tumors by deep learning-based models on magnetic resonance images using a developed web-based interface

Konuralp Tıp Dergisi ◽

10.18521/ktd.889777 ◽

2021 ◽

Author(s):

Bora TETİK ◽

Hasan UCUZAL ◽

Şeyma YAŞAR ◽

Cemil ÇOLAK

Keyword(s):

Deep Learning ◽

Magnetic Resonance ◽

Brain Tumors ◽

Magnetic Resonance Images ◽

Automated Classification ◽

Web Based

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Deep learning-based classification of resting-state fMRI independent-component analysis

10.1101/2020.07.02.183772 ◽

2020 ◽

Author(s):

Victor Nozais ◽

Philippe Boutinaud ◽

Violaine Verrecchia ◽

Marie-Fateye Gueye ◽

Pierre Yves Hervé ◽

...

Keyword(s):

Deep Learning ◽

Resting State ◽

Resting State Fmri ◽

Brain Regions ◽

Independent Component ◽

Angular Gyrus ◽

Automated Classification ◽

Dimensional Parameter ◽

Spatial Level

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here, we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants respectively. We trained a multi-layer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92%. Predictions on the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96% of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

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