Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,eval = FALSE,echo = T)
## -----------------------------------------------------------------------------
# URLs_SIIM_SMALL()
## -----------------------------------------------------------------------------
# library(fastai)
# library(magrittr)
# library(zeallot)
#
# items = get_dicom_files("siim_small/train/")
# items
#
# c(trn,val) %<-% RandomSplitter()(items)
#
# patient = 7
# xray_sample = dcmread(items[patient])
#
# xray_sample %>% show() %>% plot()
## -----------------------------------------------------------------------------
# # gather data
# items_list = items$items
#
# dicom_dataframe = data.frame()
#
# for(i in 1:length(items_list)) {
# res = dcmread(as.character(items_list[[i]])) %>% to_matrix(matrix = FALSE)
# dicom_dataframe = dicom_dataframe %>% rbind(res)
# if(i %% 50 == 0) {
# print(i)
# }
# }
## -----------------------------------------------------------------------------
# df = data.table::fread("siim_small/labels.csv")
#
# pneumothorax = DataBlock(blocks = list(ImageBlock(cls = Dicom()), CategoryBlock()),
# get_x = function(x) {paste('siim_small', x[[1]], sep = '/')},
# get_y = function(x) {paste(x[[2]])},
# batch_tfms = list(aug_transforms(size = 224),
# Normalize_from_stats( imagenet_stats() )
# ))
#
# dls = pneumothorax %>% dataloaders(as.matrix(df))
#
# dls %>% show_batch(max_n = 16)
## -----------------------------------------------------------------------------
# learn = cnn_learner(dls, resnet34(), metrics = accuracy)
# summary(learn)
## -----------------------------------------------------------------------------
# learn %>% fit_one_cycle(3)
## -----------------------------------------------------------------------------
# learn %>% show_results()
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