DOI: 10.5281/zenodo.10684867
title: Feasibility of deep learning-based cancer detection in ultrasound microvascular images: Dataset

description: This repository provides the data and results for an upcoming publication that evaluates the feasibility of applying convolutional neural networks towards cancer detection in acoustic angiography volumes, acquired in vivo. The associated code for model training and hyperparameter tuning in a nested k-fold cross-validation study can be found in the linked GitHub repository. 

The all_data.zip file contains three primary folders outlined below:



FOR_TRAINING - .npz files containing 2-D or 3-D labeled datasets, packaged for training



2d_data.npz: lateral-elevation maximum intensity projections

3d_data.npz: full volumes



MAT_FILES - raw .mat files for individual volume acquisitions that can be loaded into MATLAB  



bmodes - high frequency B-modes of the tumor volumes



each volume acquisition .mat file is prefixed with "PR" containing:



ax: axial dimension array [mm]

lat: lateral dimension array [mm]

scan: elevation dimension array [mm]

bmode: bmode volume, axial x lateral x elevation

imtor: acoustic angiography volume, axial x lateral x elevation

tormip: lateral-elevation maximum intensity projection



files2dataset.mat associates the .mat files in the folder to the packaged .npz datasets



files: a cell array of file names

dataset_idx: 0-based indexes corresponding to the data in the .npz datasets

tumor_sizes: measured tumor sizes [mm], defined as the longest diameter of the tumor





controls 



each volume acquisition .mat file is prefixed with "PR" containing:



out - a structure containing the following fields:



ax: axial dimension array [mm]

lat: lateral dimension array [mm]

scan: elevation dimension array [mm]

imtor: acoustic angiography volume, axial x lateral x elevation





files2dataset.mat associates the .mat files in the folder to the packaged .npz datasets



files: a cell array of file names

dataset_idx: 0-based indexes corresponding to the data in the .npz datasets





tumors



each volume acquisition .mat file is prefixed with "PR" containing:



out - a structure containing the following fields:



ax: axial dimension array [mm]

lat: lateral dimension array [mm]

scan: elevation dimension array [mm]

imtor: acoustic angiography volume, axial x lateral x elevation





files2dataset.mat associates the .mat files in the folder to the packaged .npz datasets



files: a cell array of file names

dataset_idx: 0-based indexes corresponding to the data in the .npz datasets







TRAINED_MODELS - folders containing the final trained 2-D and 3-D models of the nested k-fold cross-validation study (for each outer fold)



best_acc.config - model configuration with the best accuracy after hyperparameter tuning

best_acc.pt - model with the best accuracy after final training on the outer fold

best_loss.config - model configuration with the best loss after hyperparameter tuning

best_loss.pt - model with the best loss after final training on the outer fold

val_acc.dat - file to keep track of the best validation accuracy score

val_loss.dat - file to keep track of the best validation loss score