A metric suite leveraging the logical inference capabilities of LLMs, for radiology report generation both with and without grounding

CLIP-Lung (MICCAI 2023)

This is a list of awesome prototype-based papers for explainable artificial intelligence.

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

Official implementation for AnomalyCLIP (ICLR 2024)

A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities

[ICCV 2023] CLIP-Driven Universal Model; Rank first in MSD Competition.

An object relational mapping for the LIDC dataset using sqlalchemy.

A curated list of foundation models for vision and language tasks in medical imaging

Official Code Repository for Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses (X-Caps)

RadIO is a library for data science research of computed tomography imaging

[Official Repo: TCBB 2023] SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention

Automated Extraction and Classification of Pulmonary Lung Nodules from CT Scans

Clinically-Interpretable Radiomics [MICCAI'22, CMPB'21]

[IEEE JBHI] Reinventing 2D Convolutions for 3D Images - 1 line of code to convert pretrained 2D models to 3D!

LUNA16-Lung-Nodule-Analysis-2016-Challenge

Implementation of "Multi-scale Gradual Itegration Convolutional Neural Network for False Positive Reduction in Pulmonary Nodule Detection"

Implementation of "Multi-scale Gradual Itegration Convolutional Neural Network for False Positive Reduction in Pulmonary Nodule Detection"

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

Template for nodule detection algorithm for node21 challenge

nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that …

Self-Supervised Learning for OOD Detection (NeurIPS 2019)

Testing a model performance for CIFAR10-C

Corruption and Perturbation Robustness (ICLR 2019)

PyTorch implementation of adversarial attacks [torchattacks]

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)