wahyurahmaniar

STPM_anomaly_detection

Unofficial pytorch implementation of Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection

SimpleNet

PRISM

[MICCAI 2024 Early Acceptance] PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts

Lung_Nodule_Classification

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"

FastFlow

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

iml-dl

Deep Learning Framework

MorphAEus

What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection

PHANES

Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection

MADDR_code

BMAD

BMAD hold a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license

DeepLung

WACV18 paper "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification"

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

pyllama

LLaMA: Open and Efficient Foundation Language Models

test-time-adaptable-neural-networks-for-domain-generalization

Code for the paper "Test-time adaptable neural networks for robust medical image segmentation"

basilisk

Astrodynamics simulation framework

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).

llama

Inference code for Llama models

AG-ReID

VanillaNet

wormhole

A wormhole simulation.

PHiSeg-code

Tensorflow Code for "PHiSeg: Capturing Uncertainty in Medical Image Segmentation", Proc. MICCAI 2019

NoduleNet

[MICCAI' 19] NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation

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

ijepa

Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive architecture."