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Datasets
cathy-stones/amos
A large-scale, diverse, clinical dataset for abdominal organ segmentation.
cathy-stones/birl
The initial dataset of stained histological tissues is composed by image pairs of related sections (mainly, consecutive cuts). Each image in the pair is coloured with a different stain.
1
cathy-stones/brats-2015
The BraTS 2015 dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. The four MRI modalities are T1, T1c, T2, and T2FLAIR. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis.
cathy-stones/brats-2017
The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation.
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cathy-stones/camus
This project aims to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views).
cathy-stones/chase-db1
CHASE_DB1 is a dataset for retinal vessel segmentation which contains 28 color retina images with the size of 999×960 pixels which are collected from both left and right eyes of 14 school children. Each image is annotated by two independent human experts.
cathy-stones/cord-19
CORD-19 is a free resource of tens of thousands of scholarly articles about COVID-19, SARS-CoV-2, and related coronaviruses for use by the global research community.
cathy-stones/chexpert
A large dataset that contains 224,316 chest radiographs of 65,240 patients.
cathy-stones/chestx-ray14
ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques.
1
cathy-stones/chestx-ray8
ChestX-ray8 is a medical imaging dataset which comprises 108,948 frontal-view X-ray images of 32,717 (collected from the year of 1992 to 2015) unique patients with the text-mined eight common disease labels, mined from the text radiological reports via NLP techniques.
cathy-stones/cholec80
Cholec80 is an endoscopic video dataset containing 80 videos of cholecystectomy surgeries performed by 13 surgeons. The videos are captured at 25 fps and downsampled to 1 fps for processing.
cathy-stones/consep
The colorectal nuclear segmentation and phenotypes (CoNSeP) dataset consists of 41 H&E stained image tiles, each of size 1,000×1,000 pixels at 40× objective magnification. The images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs
cathy-stones/drive
The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation.
1
cathy-stones/glas
The dataset used in this challenge consists of 165 images derived from 16 H&E stained histological sections of stage T3 or T42 colorectal adenocarcinoma.
cathy-stones/ham10000
HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities.
1
cathy-stones/kvasir-seg
Kvasir-SEG is an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical doctor and then verified by an experienced gastroenterologist.
cathy-stones/luna
The LUNA challenges provide datasets for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.
cathy-stones/luna16
The LUNA16 (LUng Nodule Analysis) dataset is a dataset for lung segmentation. It consists of 1,186 lung nodules annotated in 888 CT scans.
cathy-stones/mimic-cxr
MIMIC-CXR from Massachusetts Institute of Technology presents 371,920 chest X-rays associated with 227,943 imaging studies from 65,079 patients. The studies were performed at Beth Israel Deaconess Medical Center in Boston, MA.
cathy-stones/medical-segment
The Medical Segmentation Decathlon is a collection of medical image segmentation datasets. It contains a total of 2,633 three-dimensional images collected across multiple anatomies of interest, multiple modalities and multiple sources. Specifically, it contains data for the following body organs or parts: Brain, Heart, Liver, Hippocampus, Prostate, Lung, Pancreas, Hepatic Vessel, Spleen and Colon.
cathy-stones/pcam
The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections.
cathy-stones/ppmi
The Parkinson’s Progression Markers Initiative (PPMI) dataset originates from an observational clinical and longitudinal study comprising evaluations of people with Parkinson’s disease (PD), those people with high risk, and those who are healthy.
cathy-stones/promise12
The PROMISE12 dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols.
cathy-stones/padchest
PadChest is a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports.
cathy-stones/physionet-challenge-2012
cathy-stones/stare
The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.
cathy-stones/sleep-edf
The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature.
cathy-stones/ubfc-rppg
A CMS50E transmissive pulse oximeter was used to obtain the ground truth PPG data comprising the PPG waveform as well as the PPG heart rates.
cathy-stones/vqa-rad
VQA-RAD consists of 3,515 question–answer pairs on 315 radiology images.
cathy-stones/mimic-website
1
Models
cathy-stones/asclepius
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
cathy-stones/biomedlm
BioMedLM 2.7B is new language model trained exclusively on biomedical abstracts and papers from The Pile.
cathy-stones/biomistral-7b
A Collection of Open-Source Pretrained Large Language Models for Medical Domains
cathy-stones/biomedclip
BiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, a dataset of 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning.
1
cathy-stones/cnn-design-for-ad
This repository contains code for a medical paper and a machine learning paper on deep learning for dementia.
cathy-stones/chatdoctor
Autonomous ChatDoctor with Disease Database Demo.
cathy-stones/clinicalbert
This model card describes the ClinicalBERT model, which was trained on a large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed. We then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model.
cathy-stones/duck-net
polyp image segmentation
cathy-stones/fewshot-gan-unet3d
Few-shot 3D Medical Image Segmentation using Generative Adversarial Learning
2
cathy-stones/llava-med
Large Language and Vision Assistant for BioMedicine
cathy-stones/livianet
3D fully Convolutional Neural Network for semantic image segmentation
1
4
cathy-stones/med-palm
A responsible path to generative AI in healthcare: Unleash the power of Med-PaLM 2 to revolutionize medical knowledge, answer complex questions, and enhance healthcare experiences with accuracy, safety, and equitable practices.
cathy-stones/medical-chatbot
Please note that the chatbot is designed for research purposes only and is not intended for use in real medical settings.
cathy-stones/medical-ner
This model is a fine-tuned version of DeBERTa on the PubMED Dataset.
1
cathy-stones/moleanalysis
We trained our model on cancerous me-cells using Huawei ML Kit Custom Model Generation with our Mole Analysis application, which will help you in your diagnosis and follow-up.
cathy-stones/pmc-llama
Towards Building Open-source Language Models for Medicine
cathy-stones/alphafold
This package provides an implementation of the inference pipeline of AlphaFold v2.
1
cathy-stones/brats-mri-segmentation
cathy-stones/chexpert-labeler
CheXpert NLP tool to extract observations from radiology reports.
3
cathy-stones/codoc
This repository includes the source code for the paper "Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians (CoDoC)" by Dvijotham et al. (2023), published in the journal Nature Medicine.
cathy-stones/deepvariant-r1-6-1
DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file.
cathy-stones/endoscopic-segmentation
A pre-trained model for the endoscopic tool segmentation task, trained using a flexible unet structure with an efficientnet-b2 [1] as the backbone and a UNet architecture [2] as the decoder.
cathy-stones/medalpaca
MedAlpaca expands upon both Stanford Alpaca and AlpacaLoRA to offer an advanced suite of large language models specifically fine-tuned for medical question-answering and dialogue applications. Our primary objective is to deliver an array of open-source language models, paving the way for seamless development of medical chatbot solutions.
cathy-stones/medical-tokenizer
clinitokenizer is a sentence tokenizer for clinical text to split unstructured text from clinical text (such as Electronic Medical Records) into individual sentences.
cathy-stones/medicine-llm
Domain Adaptation of Large Language Models
cathy-stones/meditron
Meditron is a suite of open-source medical Large Language Models (LLMs).
cathy-stones/pathology-nuclei-classification
A pre-trained model for classifying nuclei cells
cathy-stones/pathology-tumor-detection
A pre-trained model for automated detection of metastases in whole-slide histopathology images.
cathy-stones/segment-anything
Segment Anything Model for Medical Image Analysis: an Experimental Study
cathy-stones/wholebody-ct-segmentation
Models for (3D) segmentation of 104 whole-body segments.