maxboels/Breast-Cancer-NN-Classification-Radiomics

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Created almost 4 years ago
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maxboels/Breast-Cancer-NN-Classification-Radiomics

maxboels

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Shallow neural networks for breast cancer classification as either benign or cancerous tumors.

Surgical-Phase-Recognition

Collated a list of useful open-access work related to surgical phase recognition and surgical skills and workflow analysis.

video-action-recognition-datasets

This repository contains video datasets that can be used for training coarse to fine-grained (phase, step and action) temporal classification tasks.

3D-Unet-for-Segmentation-of-Lung-lobes-in-CT-volumes

3D U-Net for Segmentation of Lung lobes in CT volumes - Keras/Tensorflow

Jupyter Notebook

Predicting-Breast-Cancer-Malignancy-from-X-rays

Regarding the data, all 628 screening mammograms in this project have been classified as ab-normal by 2 radiologists and thus require a biopsy. Radiologists are cautious during screenings, the consequences of having a false negative push them to send women for biopsy if there is the slight-est doubt for them to have cancer. As explained, this results in a high number of false positive since the ‘cost’ is lower than having a false negative. The dataset used in this project comes from the OPTIMAM Medical Image Database, which collects NHS Breast Screening Programme (NHSB-SO) images in the UK. A deep learning approach is used to classify abnormal screenings as either malignant or benign cancer with a certain probability. Transfer Learning makes it possible to obtain high performances on small datasets. This project achieved a ROC of 80%, 86% sensitivity, and 77% NPV, which were reached with a pre-trained ResNet50v2, a state-of-the-art neural network optimized through fine-tuning hy-perparameters and data pre-processing.

Jupyter Notebook

AI_for_Medecine

Classification and Segmentation: Diagnose diseases from x-rays and 3D MRI brain images. Predict patient survival rates more accurately using tree-based models. Estimate treatment effects on patients using data from randomized trials.

Jupyter Notebook

Automatic-Speech-Recognition-with-Hidden-Markov-model

This project attempts to train a Continuous Density Hidden Markov Model (CD-HMM) for speech recognition, and is developed with Matlab software. This objective is reached using the Expectation-Maximization approach using the Baum-Welch equations. The training process uses two steps which are computing the Expectations (E-step) and Maximizing those expectations by re-estimation of the parameters (M-step). The methodology and results are discussed to provide a clear understanding of the motivations and limits of this project.

TensorFlow-Advanced-Techniques-Specialization

Jupyter Notebook

Pattern-Classification-Problem-with-Bayesian-Approach

Two popular classifiers are investigated; the Bayes decision Rule for normally distributed classes and the k-Nearest Neighbour decision rule.

Image-Retrieval-Computer-Vision

This project is part of the Computer Vision and Pattern Recognition module at University of Surrey. Students are required to use Matlab to implement this project. The dataset gathers 591 images of 20 classes of different objects e.g. sheep, cow, boat, car and are in RGB color format with different number of pixels. The objective is to develop a visual search engine to retrieve similar images to the one provided by the user. This query image is selected from the data set and thus corresponds to one of the 20 classes.

Lung-Cancer-Life-Expectancy-NN-Regression

Predicting lung cancer survival time

Jupyter Notebook

Speech-Synthesis-with-Linear-Predictive-Coding

This project attempts to solve the problem of speech synthesis of male and female vowels, and is developed with the help of Matlab software. This objective is reached using the Linear Predictive Code approach to estimate the coefficients and formant frequency. Then, vowels are generated by passing an excitation signal through the modeled filter.