scholarly journals Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging

2020 ◽  
Vol 8 (2) ◽  
pp. 139-148
Author(s):  
Mani Manavalan
Keyword(s):  
High Resolution ◽  
Basal Cell ◽  
Classification Performance ◽  
Computer Assisted ◽  
Learning Approaches ◽  
Disease Class ◽  
Histological Sections ◽  
Basal Cell Carcinomas ◽  
Improved Performance ◽  
Clinical Records

The most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today's state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance.

High-Resolution MRI of Basal Cell Carcinomas of the Face Using a Microscopy Coil

2007 ◽  
Vol 188 (5) ◽  
pp. W480-W484 ◽  
Author(s):  
Hubert Gufler ◽  
Folker E. Franke ◽  
Wigbert S. Rau
Keyword(s):  
High Resolution ◽  
Basal Cell ◽  
Microscopy Coil ◽  
High Resolution Mri ◽  
Basal Cell Carcinomas ◽  
The Face

scholarly journals A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1451-1454 ◽  
Author(s):  
C. Zhang ◽  
X. Pan ◽  
S. Q. Zhang ◽  
H. P. Li ◽  
P. M. Atkinson
Keyword(s):  
High Resolution ◽  
Decision Tree ◽  
Image Classification ◽  
Rough Set ◽  
Remotely Sensed ◽  
Classification Performance ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Remotely Sensed Image ◽  
Very High

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. <br><br> This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering

2017 ◽  
Vol 56 (03) ◽  
pp. 209-216 ◽  
Author(s):  
Said Ouatik El Alaoui ◽  
Mourad Sarrouti
Keyword(s):  
Machine Learning ◽  
Question Answering ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Question Type ◽  
Support Vector ◽  
Learning Approaches ◽  
Answer Extraction ◽  
Improved Performance ◽  
Type Classification

SummaryBackground and Objective: Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary.Methods: In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine- learning algorithms. Finally, the class label is predicted using the trained classifiers.Results: Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features’ provider of support vector machine (SVM) lead to the highest accuracy of 89.40%.Conclusion: The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

scholarly journals 452 Neural networks detect cutaneous basal cell carcinomas in histological sections

2019 ◽  
Vol 139 (9) ◽  
pp. S292
Author(s):  
S. Kimeswenger ◽  
G. Klambauer ◽  
G. Lang ◽  
M. Hofmarcher ◽  
P. Tschandl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Basal Cell ◽  
Histological Sections ◽  
Basal Cell Carcinomas

Scalpel excision of basal cell carcinomas

1978 ◽  
Vol 114 (5) ◽  
pp. 739-742 ◽  
Author(s):  
R. S. Bart
Keyword(s):  
Basal Cell ◽  
Basal Cell Carcinomas

Follicular atrophoderma with multiple basal cell carcinomas. An abortive form of bazex syndrome.

1989 ◽  
Vol 51 (2) ◽  
pp. 250-255
Author(s):  
Seiji ARASE ◽  
Hideki NAKANISHI ◽  
Shin HARADA ◽  
Fumio SHIGEMI ◽  
Katsuyuki TAKEDA
Keyword(s):  
Basal Cell ◽  
Basal Cell Carcinomas ◽  
Multiple Basal Cell Carcinomas
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