An interactive learning framework for scalable classification of pathology images

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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Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

Cancers ◽

10.3390/cancers13194875 ◽

2021 ◽

Vol 13 (19) ◽

pp. 4875

Author(s):

Matteo Bulloni ◽

Giada Sandrini ◽

Irene Stacchiotti ◽

Massimo Barberis ◽

Fiorella Calabrese ◽

...

Keyword(s):

Automated Analysis ◽

Labelling Index ◽

Neuroendocrine Neoplasms ◽

Proliferating Cells ◽

Ki 67 ◽

Learning Framework ◽

Diagnosis And Prognosis ◽

Tumour Prognosis

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

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