Wahyu Rahmaniar's starred repositories
STPM_anomaly_detection
Unofficial pytorch implementation of Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection
cervical_cancer_detection
A deep learning framework for cervical cancer detection to allow improved accuracy for PAP smear test results
patchcore-few-shot
Implementation of our paper "Optimizing PatchCore for Few/many-shot Anomaly Detection"
Neural-Network-Parameter-Diffusion
We introduce a novel approach for parameter generation, named neural network parameter diffusion (p-diff), which employs a standard latent diffusion model to synthesize a new set of parameters
Partial-Order-Pruning
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
local-global
Official implementation of Lung Nodule Classification using Deep Local-Global Networks using PyTorch
LIDC-IDRI-Preprocessing
This is the preprocessing step of the LIDC-IDRI dataset
test-time-adaptable-neural-networks-for-domain-generalization
Code for the paper "Test-time adaptable neural networks for robust medical image segmentation"
Astronomical-Images-Classification
Recently, a massive astronomical dataset is being collected to find answers for a variety of unanswered questions about our universe by virtue of modern sky survey instruments. Unfortunately, it is impossible to work on these massive datasets manually to get effective results so, astronomers are seeking approaches to automate the human error borne processes of manual scanning in order to discover astronomical knowledge and information from these large raw datasets i.e. to classify stars, quasars, galaxies and Supernovae (SNe). The problem here, this is done by hand and it is a very time consuming job as well as it is subject to human bias which differs from person to person. In addition, the manual scanning is infeasible for a huge amount of images. From this point of view, I've selected this concrete astronomical classification problem to investigate applying convolutional Neural Networks (CNNs) algorithm to automate this process and then I compared my results to a reference publication as a benchmark model by using the same well-known public dataset of the Sloan Digital Sky Survey (SDSS).
PHiSeg-code
Tensorflow Code for "PHiSeg: Capturing Uncertainty in Medical Image Segmentation", Proc. MICCAI 2019
MedicalNet
Many studies have shown that the performance on deep learning is significantly affected by volume of training data. The MedicalNet project provides a series of 3D-ResNet pre-trained models and relative code.
SyntheticTumors
[CVPR 2023] Label-Free Liver Tumor Segmentation