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Bioresponsive Nanoparticles
Published in Deepa H. Patel, Bioresponsive Polymers, 2020
Drashti Pathak, Deepa H. Patel
For example, according to the National Breast Cancer Foundation, the fight against breast cancer is not a fight against a single disease, but a fight against no less than seven major subclasses of breast cancer (Ductal Carcinoma In-situ, Infiltrating Ductal Carcinoma, Medullary Carcinoma, Infiltrating Lobular Carcinoma, Tubular Carcinoma, Mucinous Carcinoma, and Inflammatory Breast Cancer). Each of these types of breast cancer are classified based on what type of tissue the cancer first develops in as well as physical characteristics of the tumors or cancer cells themselves.
The transition module: a method for preventing overfitting in convolutional neural networks
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2019
S. Akbar, M. Peikari, S. Salama, S. Nofech-Mozes, A. L. Martel
We evaluated the performance of the proposed transition module on two independent data-sets. Some example patches from each data-set are shown in Figure 3. A description of each data-set is as follows.In-House: an in-house data-set acquired from the Department of Anatomic Pathology in Sunnybrook Health Sciences Centre. This data-set comprises of 1229 image patches extracted and labelled from breast WSIs scanned at x40 objective by a Scanscope XT (Aperio technologies, Leica Biosystems) scanner. Each RGB patch of size was hand selected from 31 WSIs, each one from a single patient, by a trained pathologist. The surgical excision specimens represent sections from residual invasive and in situ breast carcinoma after presurgical systemic therapy (also known as neo-adjuvant therapy). Each image patch was confirmed to contain either malignant or benign tissue by an expert pathologist. ‘Benign’ refers to patches which are absent of cancer cells but may contain epithelial and stromal elements with normal or spectrum of benign changes (Figure 3). 5-fold cross validation was used to evaluate the performance over 100 epochs.BreaKHis: a public data-set, BreaKHis (Spanhol et al. 2016b) which contains scanned images of benign (adenosis, fibroadenoma, phyllodes tumour, tubular adenoma) and malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, papillary carcinoma) breast tumours at 40X magnification. Images were resampled into patches of dimensions , suitable for the CNNs we adopted, resulting in 11, 800 image patches in total. BreaKHis was validated using 2-fold cross-validation and across 30 epochs.MNIST, CIFAR-10: two publically available data-sets containing greyscale 2424 pixel images of handwritten digits from 0 to 9 (MNIST), and RGB 3232 pixel images of random everyday objects such as airplanes and cats divided into 10 classes (CIFAR-10). As images in these data-sets are considerably smaller than the two data-sets described above, we opted to train LeNet-5 (LeCun et al. 1998) CNN architectures which originally has two convolution layers. Each data-set has an independent train and test set and we trained the network for 30 epochs.