secondbear's Stars
EFS-OpenSource/calibration-framework
The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
AIgen/df-posthoc-calibration
Model-agnostic posthoc calibration without distributional assumptions
ENSTA-U2IS-AI/torch-uncertainty
Open-source framework for uncertainty and deep learning models in PyTorch :seedling:
yandexdataschool/roc_comparison
The fast version of DeLong's method for computing the covariance of unadjusted AUC.
AntoniDabrowski/RI-DASH
Summary of 'genomic analysis' conducted at the Right Information company. Code for our portfolio page.
ErdanC/Tooth-and-alveolar-bone-segmentation-from-CBCT
Novartis/torchsurv
Deep survival analysis made easy
bhkann/DualNet-ENE
Extranodal Extension (ENE) Identification on Computed Tomography with Deep Learning for Head and Neck Cancers
AIM-Harvard/foundation-cancer-image-biomarker
Code and evaluation repository for the paper
karpathy/nanoGPT
The simplest, fastest repository for training/finetuning medium-sized GPTs.
aai-institute/beyond-jupyter
Software design principles for machine learning applications
openai/CLIP
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
wilicc/gpu-burn
Multi-GPU CUDA stress test
hustvl/Vim
[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
facebookresearch/ConvNeXt-V2
Code release for ConvNeXt V2 model
pytorch/captum
Model interpretability and understanding for PyTorch
MECLabTUDA/M3d-Cam
xiaohong1/COVID-ViT
lucidrains/vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
acoadmarmon/covid-autoencoder-cv
We propose an unsupervised learning approach that can be tied back to existing metadata, like mortality, age, BMI, etc. To accomplish this, we will train an Autoencoder model to create a low-dimensional representation of each image (Bank et al. 2020), and then use different clustering methods to determine optimal groupings for these images based on their encoding (Song et al. 2013)(Guo et al. 2017). Once these groups are instantiated, we can then associate image metadata to each cluster to determine whether there are statistically significant attributes tied to specific clusters. If it could be proven that attributes like mortality rate or success with intubation are linked to certain clusters, that information could be incredibly valuable for clinical outcomes. Also, although we have limited prognosis labels, we will also determine autoencoder performance by trying to classify the image based on the encoding using fully connected layers.
MIC-DKFZ/nnDetection
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 were used to develop and evaluate the performance of the proposed method.
shamangary/Pytorch-Stochastic-Depth-Resnet
Pytorch Implementation of Deep Networks with Stochastic Depth
yueatsprograms/Stochastic_Depth
Deep Networks with Stochastic Depth
vanAmsterdam/deep-survival
dmis-lab/VAECox
ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data
yhygao/UTNet
Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
Heng14/3D_RP-Net
gevaertlab/MultiModalBrainSurvival
MIC-DKFZ/nnUNet
liaohaofu/adn
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction