maxboels
PhD Candidate in Computer Vision & ML at King's College London
King's College LondonLondon, UK
Pinned Repositories
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.
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.
Breast-Cancer-NN-Classification-Radiomics
Shallow neural networks for breast cancer classification as either benign or cancerous tumors.
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
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.
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.
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.
Surgical-Phase-Recognition
Collated a list of useful open-access work related to surgical phase recognition and surgical skills and workflow analysis.
TensorFlow-Advanced-Techniques-Specialization
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.
maxboels's Repositories
maxboels/Surgical-Phase-Recognition
Collated a list of useful open-access work related to surgical phase recognition and surgical skills and workflow analysis.
maxboels/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.
maxboels/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.
maxboels/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.
maxboels/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.
maxboels/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
maxboels/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.
maxboels/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.
maxboels/TensorFlow-Advanced-Techniques-Specialization
maxboels/Breast-Cancer-NN-Classification-Radiomics
Shallow neural networks for breast cancer classification as either benign or cancerous tumors.
maxboels/Lung-Cancer-Life-Expectancy-NN-Regression
Predicting lung cancer survival time
maxboels/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.
maxboels/maxboels_old.github.io
Personal website @ https://maxboels.com
maxboels/amigolab.github.io
AMIGO website
maxboels/Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
maxboels/cs231n.github.io
Public facing notes page
maxboels/dino
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
maxboels/EVG
maxboels/FATSnet
Temporally smooth online action detection using cycle-consistent future anticipation
maxboels/gpt-sparks
having fun with gpt
maxboels/how-to-guide
maxboels/maxboels
About me
maxboels/MIScnn
A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
maxboels/MONAI
AI Toolkit for Healthcare Imaging
maxboels/NYU-DLSP21
NYU Deep Learning Spring 2021
maxboels/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
maxboels/supra
maxboels/unet-1
Keras implementation of a 2D/3D U-Net with Additive Attention, Inception, and Recurrence functions provided
maxboels/VS-Code
maxboels/VStudio-Code