complete the model package

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sample_data/A/sample_A4.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,49 @@
+---
+tags:
+- monai
+- medical
+library_name: monai
+license: unknown
+---
+# Description
+A pre-trained model for breast-density classification.
+
+# Model Overview
+This model is trained using transfer learning on InceptionV3. The model weights were fine tuned using the Mayo Clinic Data. The details of training and data is outlined in https://arxiv.org/abs/2202.08238.
+
+The bundle does not support torchscript.
+
+# Input and Output Formats
+The input image should have the size [3, 299, 299]. The output is an array with probabilities for each of the four class.
+
+# Sample Data
+In the folder `sample_data` few example input images are stored for each category of images. These images are stored in jpeg format for sharing purpose.
+
+# Input and Output Formats
+The input image should have the size [299, 299, 3]. For a dicom image which are single channel. The channel can be repeated 3 times.
+The output is an array with probabilities for each of the four class.
+
+# Commands Example
+Create a json file with names of all the input files. Execute the following command
+```
+python scripts/create_dataset.py -base_dir <path to the bundle root dir>/sample_data -output_file configs/sample_image_data.json
+```
+Change the `filename` for the field `data` with the absolute path for `sample_image_data.json`
+
+
+# Add scripts folder to your python path as follows
+```
+export PYTHONPATH=$PYTHONPATH:<path to the bundle root dir>/scripts
+```
+
+# Execute Inference
+The inference can be executed as follows
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json configs/logging.conf
+```
+
+# Execute training
+It is a work in progress and will be shared in the next version soon.
+
+# Contributors
+This model is made available from Center for Augmented Intelligence in Imaging, Mayo Clinic Florida. For questions email Vikash Gupta (gupta.vikash@mayo.edu).

configs/inference.json

@@ -0,0 +1,116 @@
+{
+    "import": [
+        "$import glob",
+        "$import os",
+        "$import torchvision"
+    ],
+    "bundle_root": ".",
+    "model_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/output'",
+    "data": {
+        "_target_": "scripts.createList.CreateImageLabelList",
+        "filename": "./configs/sample_image_data.json"
+    },
+    "test_imagelist": "$@data.create_dataset('Test')[0]",
+    "test_labellist": "$@data.create_dataset('Test')[1]",
+    "dataset": {
+        "_target_": "CacheDataset",
+        "data": "$[{'image': i, 'label': l} for i, l in zip(@test_imagelist, @test_labellist)]",
+        "transform": "@preprocessing",
+        "cache_rate": 1,
+        "num_workers": 4
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 4,
+        "shuffle": false,
+        "num_workers": 4
+    },
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "TorchVisionFCModel",
+        "model_name": "inception_v3",
+        "num_classes": 4,
+        "pool": null,
+        "use_conv": false,
+        "bias": true,
+        "pretrained": true
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": "image"
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": "image",
+                "channel_dim": 2
+            },
+            {
+                "_target_": "ScaleIntensityd",
+                "keys": "image",
+                "minv": 0.0,
+                "maxv": 1.0
+            },
+            {
+                "_target_": "Resized",
+                "keys": "image",
+                "spatial_size": [
+                    299,
+                    299
+                ]
+            }
+        ]
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "sigmoid": true
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@model_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false,
+            "output_transform": "$lambda x: None"
+        },
+        {
+            "_target_": "ClassificationSaver",
+            "output_dir": "@output_dir",
+            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
+            "output_transform": "$monai.handlers.from_engine(['pred'])"
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@evaluator.run()"
+    ]
+}

configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

@@ -0,0 +1,75 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "changelog": {
+        "0.1.0": "complete the model package"
+    },
+    "version": "0.1.0",
+    "monai_version": "1.0.0",
+    "pytorch_version": "1.12.1",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {
+        "torchvision": "0.13.1"
+    },
+    "task": "Breast Density Classification",
+    "description": "A pre-trained model for classifying breast images (mammograms)  ",
+    "authors": "Center for Augmented Intelligence in Imaging, Mayo Clinic Florida",
+    "copyright": "Copyright (c) Mayo Clinic",
+    "data_source": "Mayo Clinic ",
+    "data_type": "Jpeg",
+    "image_classes": "three channel data, intensity scaled to [0, 1]. A single grayscale is copied to 3 channels",
+    "label_classes": "four classes marked as [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0] and [0, 0, 0, 1] for the classes A, B, C and D respectively.",
+    "pred_classes": "One hot data",
+    "eval_metrics": {
+        "accuracy": 0.96
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "Gupta, Vikash, et al. A multi-reconstruction study of breast density estimation using Deep Learning. arXiv preprint arXiv:2202.08238 (2022)."
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "magnitude",
+                "modality": "Mammogram",
+                "num_channels": 3,
+                "spatial_shape": [
+                    229,
+                    229
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "labels",
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "num_channels": 4,
+                "spatial_shape": [
+                    1,
+                    4
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "A",
+                    "1": "B",
+                    "2": "C",
+                    "3": "D"
+                }
+            }
+        }
+    }
+}

docs/README.md

@@ -0,0 +1,42 @@
+# Description
+A pre-trained model for breast-density classification.
+
+# Model Overview
+This model is trained using transfer learning on InceptionV3. The model weights were fine tuned using the Mayo Clinic Data. The details of training and data is outlined in https://arxiv.org/abs/2202.08238.
+
+The bundle does not support torchscript.
+
+# Input and Output Formats
+The input image should have the size [3, 299, 299]. The output is an array with probabilities for each of the four class.
+
+# Sample Data
+In the folder `sample_data` few example input images are stored for each category of images. These images are stored in jpeg format for sharing purpose.
+
+# Input and Output Formats
+The input image should have the size [299, 299, 3]. For a dicom image which are single channel. The channel can be repeated 3 times.
+The output is an array with probabilities for each of the four class.
+
+# Commands Example
+Create a json file with names of all the input files. Execute the following command
+```
+python scripts/create_dataset.py -base_dir <path to the bundle root dir>/sample_data -output_file configs/sample_image_data.json
+```
+Change the `filename` for the field `data` with the absolute path for `sample_image_data.json`
+
+
+# Add scripts folder to your python path as follows
+```
+export PYTHONPATH=$PYTHONPATH:<path to the bundle root dir>/scripts
+```
+
+# Execute Inference
+The inference can be executed as follows
+```
+python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json configs/logging.conf
+```
+
+# Execute training
+It is a work in progress and will be shared in the next version soon.
+
+# Contributors
+This model is made available from Center for Augmented Intelligence in Imaging, Mayo Clinic Florida. For questions email Vikash Gupta (gupta.vikash@mayo.edu).

docs/license.txt

@@ -0,0 +1,201 @@
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models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:710a49172243ea9972bec5dc717382c294ed77310596b6ad0df9bb83f7ceafce
+size 100826617

scripts/__init__.py

@@ -0,0 +1 @@
+from .createList import CreateImageLabelList

scripts/createList.py

@@ -0,0 +1,17 @@
+import json
+
+
+class CreateImageLabelList:
+    def __init__(self, filename):
+        self.filename = filename
+        fid = open(self.filename, "r")
+        self.json_dict = json.load(fid)
+
+    def create_dataset(self, grp):
+        image_list = []
+        label_list = []
+        image_label_list = self.json_dict[grp]
+        for _ in image_label_list:
+            image_list.append(_["image"])
+            label_list.append(_["label"])
+        return image_list, label_list

scripts/create_dataset.py

@@ -0,0 +1,42 @@
+import argparse
+import json
+import os
+import sys
+
+
+def main(base_dir: str, output_file: str):
+
+    list_classes = ["A", "B", "C", "D"]
+
+    output_list = []
+    for _class in list_classes:
+        data_dir = os.path.join(base_dir, _class)
+        list_files = os.listdir(data_dir)
+        if _class == "A":
+            _label = [1, 0, 0, 0]
+        elif _class == "B":
+            _label = [0, 1, 0, 0]
+        elif _class == "C":
+            _label = [0, 0, 1, 0]
+        elif _class == "D":
+            _label = [0, 0, 0, 1]
+
+        for _file in list_files:
+            _out = {"image": os.path.join(data_dir, _file), "label": _label}
+            output_list.append(_out)
+
+    data_dict = {"Test": output_list}
+
+    fid = open(output_file, "w")
+    json.dump(data_dict, fid, indent=1)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="")
+
+    parser.add_argument("-base_dir", "--base_dir", default="sample_data", help="dir of dataset")
+    parser.add_argument(
+        "-output_file", "--output_file", default="configs/sample_image_data.json", help="output file name"
+    )
+    parser_args, _ = parser.parse_known_args(sys.argv)
+    main(base_dir=parser_args.base_dir, output_file=parser_args.output_file)

scripts/script.py

@@ -0,0 +1,24 @@
+import os
+
+from monai.bundle.config_parser import ConfigParser
+
+os.environ["CUDA_DEVICE_IRDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+parser = ConfigParser()
+parser.read_config("../configs/inference.json")
+data = parser.get_parsed_content("data")
+device = parser.get_parsed_content("device")
+network = parser.get_parsed_content("network_def")
+
+inference = parser.get_parsed_content("evaluator")
+inference.run()
+
+print(type(network))
+
+
+datalist = parser.get_parsed_content("test_imagelist")
+print(datalist)
+
+inference = parser.get_parsed_content("evaluator")
+inference.run()