Classification of Micro-Calcification in Breast from Mammographic Images using Transfer Learning

Early detection of cancer is most important for long term survival of patient. Now a days CADx are widely used for early identification of breast cancer automatically. CAD uses significant features to identify and categorize cancer. CADx based on Convolutional Neural Network are becoming popular now a days due to extracting relevant features automatically. CNNs can be trained from scratch for medical images due to various input sizes and tumor structures. But due to limited amount of medical images available for training ,we have used transfer learning approach.We developed a deep learning framework based on CNN to discriminate the breast tumor either benign or malignant using transfer learning. We used digital mammographic images containing both views from CBIS-DDSM database. We have achived training(100%) and validation accuracy greater than 90% with minimum training and validation loss. We have also compared the reaults with transfer learning using pretrained network alexnet and googlenet on same dataset.

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Faculty Opinions recommendation of Survival After Out-of-Hospital Cardiac Arrest in Relation to Age and Early Identification of Patients With Minimal Chance of Long-Term Survival.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725381975.793527134 ◽

2017 ◽

Author(s):

Cameron Dezfulian

Keyword(s):

Cardiac Arrest ◽

Early Identification ◽

Term Survival ◽

Long Term Survival ◽

Hospital Cardiac Arrest

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CNN Based Transfer Learning Framework For Classification Of COVID-19 Disease From Chest X-ray

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) ◽

10.1109/iciccs51141.2021.9432181 ◽

2021 ◽

Author(s):

Pramit Brata Chanda ◽

Surajit Banerjee ◽

Vivek Dalai ◽

Rahul Ray

Keyword(s):

Transfer Learning ◽

X Ray ◽

Learning Framework ◽

Chest X Ray

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IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival

Neuro-Oncology ◽

10.1093/neuonc/nos317 ◽

2013 ◽

Vol 15 (4) ◽

pp. 469-479 ◽

Cited By ~ 109

Author(s):

S. Leu ◽

S. von Felten ◽

S. Frank ◽

E. Vassella ◽

I. Vajtai ◽

...

Keyword(s):

Strong Predictor ◽

Molecular Classification ◽

Low Grade Glioma ◽

Low Grade ◽

Term Survival ◽

Long Term Survival

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Classification of medical images of patients with Covid-19 using transfer learning technology of convolutional neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1801/1/012010 ◽

2021 ◽

Vol 1801 (1) ◽

pp. 012010

Author(s):

A S Miroshnichenko ◽

V M Mikhelev

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Medical Images ◽

Learning Technology

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A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.635766 ◽

2021 ◽

Vol 4 ◽

Author(s):

Ruqian Hao ◽

Khashayar Namdar ◽

Lin Liu ◽

Farzad Khalvati

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Active Learning ◽

Transfer Learning ◽

Medical Images ◽

Tumor Classification ◽

Training Data ◽

Validation Dataset ◽

Low Grade ◽

Learning Framework

Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence–enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning–based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice–based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

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Spirometric classifications of COPD severity as predictive markers for clinical outcomes: the HUNT Study

10.1101/2020.11.03.20221432 ◽

2020 ◽

Author(s):

Laxmi Bhatta ◽

Linda Leivseth ◽

Xiao-Mei Mai ◽

Anne Hildur Henriksen ◽

David Carslake ◽

...

Keyword(s):

Clinical Outcomes ◽

Continuous Variable ◽

Z Score ◽

Uk Biobank ◽

Short Term ◽

Term Survival ◽

Respiratory Mortality ◽

Hunt Study

ABSTRACTRationaleGOLD grades based on percent-predicted FEV1 poorly predicts mortality. Studies have recommended alternative expressions of FEV1 for the classification of COPD severity and they warrant investigation.ObjectiveTo compare the predictive abilities of ppFEV1 (ppFEV1 quartiles, GOLD grades, ATS/ERS grades), FEV1 z-score (FEV1 z-score quartiles, FEV1 z-score grades), FEV1.Ht-2 (FEV1.Ht-2 quartiles, FEV1.Ht-2 grades), FEV1.Ht-3 (FEV1.Ht-3 quartiles), and FEV1Q (FEV1Q quartiles) to predict clinical outcomes.MethodsPeople aged ≥40 years with COPD (n=890) who participated in the HUNT Study (1995-1997) were followed for 5 years (short-term) and up to 20.4 years (long-term). Survival analysis and time-dependent area under curve (AUC) were used to compare the predictive abilities. A regression tree approach was applied to obtain optimal cut-offs of different expressions of FEV1. The UK Biobank (n=6495) was used as a replication cohort with a 5-year follow-up.ResultsAs a continuous variable, FEV1Q had the highest AUCs for all-cause mortality (short-term 70.2, long-term 68.3), respiratory mortality (short-term 68.4, long-term 67.7), cardiovascular mortality (short-term 63.1, long-term 62.3), COPD hospitalization (short-term 71.3, long-term 70.9), and pneumonia hospitalization (short-term 67.8, long-term 66.6), followed by FEV1.Ht-2 or FEV1.Ht-3. Generally, similar results were observed for FEV1Q quartiles. The optimal cut-offs of FEV1Q had higher AUCs compared to GOLD grades for predicting short-term and long-term clinical outcomes. Similar results were found in UK Biobank.ConclusionsFEV1Q best predicted the clinical outcomes and could improve the classification of COPD severity.

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Novel molecular classification of colorectal cancer and correlation with long-term survival.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e15514 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e15514-e15514

Author(s):

Fayaz Hussain Mangi ◽

Tanweer Ahmed Shaikh ◽

Daniel Soria ◽

Jawaid Naeem Qureshi ◽

Ikram Uddin Ujjan ◽

...

Keyword(s):

Colorectal Cancer ◽

Median Survival ◽

Clinical Information ◽

Molecular Classification ◽

Limited Information ◽

Ki 67 ◽

Term Survival ◽

Long Term Survival

e15514 Background: Colorectal cancer (CRC) is a heterogeneous disease, however there is limited information available regarding molecular classification and correlation with long term clinical outcome. Methods: Over the period of 11 years (2008 to 2018) totals 435 patients of colorectal cancer were reported and their tumour blocks and complete set of clinical information was available for 201 patients. Immunohistochemistry was done for ER, PR, HER 2-neu, Ki-67, Bcl-2, E-Cadherin, P53, CEA, EGFR, and VEGF. PDL1, CDX-2 and CK 20. The biological pattern characterized by partitioned clustering method as described using R software. Survival analysis was done by using Kaplan Meier method. Results: There were 201 patients including 54.7% male and females were 45.3 %. Median survival was 28 months. Cluster analysis showed four novel clusters (Table), with major difference based on Ki67, CDX2 and p53. These classes showed difference in median survival, where common class 1 showed higher survival while common cluster 4 showed poor survival. Conclusions: There are at least four distinct molecular classes of colorectal cancer which can be potentially utilized in clinical practice. Pattern of Novel molecular classification of colorectal cancer and correlation with long term survival.[Table: see text]

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Update on Pancreatic Endocrine Tumors

Archives of Pathology & Laboratory Medicine ◽

10.5858/2006-130-963-uopet ◽

2006 ◽

Vol 130 (7) ◽

pp. 963-966 ◽

Cited By ~ 1

Author(s):

Wendy L. Frankel

Keyword(s):

Mass Effect ◽

Acinar Cell Carcinoma ◽

Ductal Adenocarcinoma ◽

Solid Pseudopapillary Tumor ◽

Endocrine Tumors ◽

Term Survival ◽

Pancreatic Endocrine Tumors ◽

Adjacent Structures

Abstract Endocrine tumors of the pancreas represent 1% to 2% of all pancreatic neoplasms. The tumors tend to have an indolent behavior, and long-term survival is common. There is no gender or age predilection. Patients can present with symptoms due to hormonal excess or a local mass effect or be asymptomatic. The tumors tend to be solid and well circumscribed. Typical microscopic findings include an organoid pattern of growth, with cells containing scant to moderate amounts of cytoplasm, and nuclei with dispersed chromatin and inconspicuous nucleoli. The morphologic spectrum of these tumors can be variable, and the differential diagnosis includes chronic pancreatitis with neuroendocrine hyperplasia, ductal adenocarcinoma, solid pseudopapillary tumor, acinar cell carcinoma, and pancreatoblastoma. The classification of these tumors remains controversial, and prognosis is difficult to predict, but important features include metastasis and invasion of adjacent structures. Resection remains the mainstay of surgical treatment. It is important to be aware that unusual morphologic variants of pancreatic endocrine tumors are common, and immunohistochemical stains can help avoid misdiagnosis.

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Cholangiocarcinoma: classification and staging

Medical Visualization ◽

10.24835/1607-0763-2018-5-55-64 ◽

2018 ◽

pp. 55-64

Author(s):

A. I. Shchegolev ◽

U. N. Tumanova ◽

G. G. Karmazanovsky ◽

O. D. Mishnev

Keyword(s):

Bile Ducts ◽

Tnm Classification ◽

Term Survival ◽

Long Term Survival ◽

Treatment Methods ◽

Disease Prognosis ◽

8Th Edition

The main classifications of cholangiocarcinoma (CC) are currently the TNM classification, as well as the Bismuth–Corlette and MSKCC classifications. The criteria of T, N and M categories and characteristics of the stages of cholangiocarcinoma of the proximal and distal bile ducts, which are specified in the modern 8th edition of the international TNM classification, are presented. TNM classification is the most common for the development of treatment methods and the determination of disease prognosis. The Bismuth–Corlette classification, which characterizes the CC of the bile ducts in the region of the gate of the liver, is used to determine the type and volume of surgery. MSKCC classification of the CC of proximal bile ducts is designed to assess the prognosis of resectability, the risk of metastases and long-term survival of patients.

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