secondbear

secondbear's Stars

• karpathy/nanoGPT

  The simplest, fastest repository for training/finetuning medium-sized GPTs.

  Language:Python38k3793206.1k

• openai/CLIP

  CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

  Language:Jupyter Notebook26.6k3244053.4k

• lucidrains/vit-pytorch

  Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

  Language:Python21.1k1562683.1k

• MIC-DKFZ/nnUNet

  Language:Python6.1k752.1k1.8k

• pytorch/captum

  Model interpretability and understanding for PyTorch

  Language:Python5k278553503

• hustvl/Vim

  [ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

  Language:Python3.1k32119208

• facebookresearch/ConvNeXt-V2

  Code release for ConvNeXt V2 model

  Language:Python1.6k773120

• wilicc/gpu-burn

  Multi-GPU CUDA stress test

  Language:C++1.5k1876302

• 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.

  Language:Python55919273102

• yueatsprograms/Stochastic_Depth

  Deep Networks with Stochastic Depth

  Language:Lua47726377

• 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.

  Language:Python35075043

• ENSTA-U2IS-AI/torch-uncertainty

  Open-source framework for uncertainty and deep learning models in PyTorch :seedling:

  Language:Python32293820

• MECLabTUDA/M3d-Cam

  Language:Python31572940

• aai-institute/beyond-jupyter

  Software design principles for machine learning applications

  Language:Jupyter Notebook3088724

• ErdanC/Tooth-and-alveolar-bone-segmentation-from-CBCT

  Language:Python219112232

• yhygao/UTNet

  Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

  Language:Python18541029

• liaohaofu/adn

  ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

  Language:Python17292540

• yandexdataschool/roc_comparison

  The fast version of DeLong's method for computing the covariance of unadjusted AUC.

  Language:Python14712840

• AIM-Harvard/foundation-cancer-image-biomarker

  [Nature Machine Intelligence 2024] Code and evaluation repository for the paper

  Language:Jupyter Notebook8852611

• Novartis/torchsurv

  Deep survival analysis made easy

  Language:Python778327

• shamangary/Pytorch-Stochastic-Depth-Resnet

  Pytorch Implementation of Deep Networks with Stochastic Depth

  Language:Python63226

• AIgen/df-posthoc-calibration

  Model-agnostic posthoc calibration without distributional assumptions

  Language:Jupyter Notebook42116

• xiaohong1/COVID-ViT

  Language:Jupyter Notebook3431011

• AntoniDabrowski/RI-DASH

  Summary of 'genomic analysis' conducted at the Right Information company. Code for our portfolio page.

  Language:Python312018

• Heng14/3D_RP-Net

  Language:Python301212

• gevaertlab/MultiModalBrainSurvival

  Language:Python18314

• dmis-lab/VAECox

  ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data

  Language:Python16339

• vanAmsterdam/deep-survival

  Language:Python60

• bhkann/DualNet-ENE

  Extranodal Extension (ENE) Identification on Computed Tomography with Deep Learning for Head and Neck Cancers

  Language:Python3101

• 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.

  Language:Jupyter Notebook2200