initial commit all file

.gitattributes

@@ -32,3 +32,4 @@ 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
+*.ts filter=lfs diff=lfs merge=lfs -text

Description.md

@@ -0,0 +1,22 @@
+## Overview
+Nuclei classification within Haematoxylin & Eosi stained histology images. Classifying nuclei cells as the following types:
+- Other
+- Inflammatory 
+- Epithelial
+- Spindle-Shaped
+
+References: https://doi.org/10.1016/j.media.2019.101563
+
+## Dataset
+The model is trained with Colorectal Nuclear Segmentation and Phenotypes (CoNSeP) dataset https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet. Images were extracted from 16 colorectal adenocarcinoma (CRA) WSIs.
+
+- Target: Nuclei
+- Task: Nuclei Cells Class Classification
+- Modality: Image (RGB)
+
+## Model Architecture
+The model is trained using DenseNet121 over CoNSep dataset.
+![alt text](file/figures/architecture.png)
+
+## Demo
+Please select or upload a nuclei histology image and label image to see Nuclei Cells Classification capabilities of this model

app.py

@@ -0,0 +1,89 @@
+import torch
+from monai.bundle import ConfigParser
+import gradio as gr
+
+parser = ConfigParser() #  load configuration files that specify various parameters for running the MONAI workflow.
+parser.read_config(f="configs/inference.json") # read the config from specified JSON file
+parser.read_meta(f="configs/metadata.json") # read the metadata from specified JSON file
+
+inference = parser.get_parsed_content("inferer")
+network = parser.get_parsed_content("network_def")
+preprocess = parser.get_parsed_content("preprocessing")
+state_dict = torch.load("models/model.pt")
+network.load_state_dict(state_dict, strict=True) #  Loads a model’s parameter dictionary
+class_names = {
+    0: "Other",
+    1: "Inflammatory",
+    2: "Epithelial",
+    3: "Spindle-Shaped",
+}
+
+def classify_image(image_file, label_file):
+    data = {"image":image_file, "label":label_file}
+    batch = preprocess(data)
+    network.eval()
+    with torch.no_grad():
+        pred = inference(batch['image'].unsqueeze(dim=0), network) # expect 4 channels input  (3 RGB, 1 Label mask)
+    prob = pred.softmax(-1).detach().cpu().numpy()[0]
+    confidences = {class_names[i]: float(prob[i]) for i in range(len(class_names))}
+    return confidences
+
+example_files1 = [
+    [r'sample_data\Images\test_11_2_0628.png',
+    r'sample_data\Labels\test_11_2_0628.png'],
+    [r'sample_data\Images\test_9_4_0149.png',
+    r'sample_data\Labels\test_9_4_0149.png'],
+    [r'sample_data\Images\test_12_3_0292.png',
+    r'sample_data\Labels\test_12_3_0292.png'],
+    [r'sample_data\Images\test_9_4_0019.png',
+    r'sample_data\Labels\test_9_4_0019.png']
+]
+
+example_files2 = [
+    [r'sample_data\Images\test_14_3_0433.png',
+    r'sample_data\Labels\test_14_3_0433.png'],
+    [r'sample_data\Images\test_14_4_0544.png',
+    r'sample_data\Labels\test_14_4_0544.png'],
+    [r'sample_data\Images\train_1_1_0095.png',
+    r'sample_data\Labels\train_1_1_0095.png'],
+    [r'sample_data\Images\train_1_3_0020.png',
+    r'sample_data\Labels\train_1_3_0020.png'],
+]
+
+with open('Description.md','r') as file:
+   markdown_content = file.read()
+with gr.Blocks() as app:
+    gr.Markdown("# Pathology Nuclei Classification")
+    gr.Markdown(markdown_content)
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                inp_img = gr.Image(type="filepath", image_mode="RGB")
+                label_img = gr.Image(type="filepath", image_mode="L")
+            with gr.Row():
+                process_btn = gr.Button(value="Process")
+                clear_btn = gr.Button(value="Clear")
+        out_txt = gr.Label(label="Probabilities", num_top_classes=4)
+
+    process_btn.click(fn=classify_image, inputs=[inp_img, label_img], outputs=out_txt)
+    clear_btn.click(lambda:(
+        gr.update(value=None),
+        gr.update(value=None),
+        gr.update(value=None)
+        ),
+        inputs=None,
+        outputs=[inp_img, label_img,out_txt]
+        )
+
+    gr.Markdown("## Image Examples")
+    with gr.Row():
+        for file in example_files1:
+            gr.Examples(
+                [file], inputs=[inp_img, label_img]
+            )
+    with gr.Row():
+        for file in example_files2:
+            gr.Examples(
+                [file], inputs=[inp_img, label_img]
+            )
+app.launch()

configs/evaluate.json

@@ -0,0 +1,75 @@
+{
+    "validate#dataset#cache_rate": 0,
+    "validate#postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "AsDiscreted",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "argmax": [
+                    true,
+                    false
+                ],
+                "to_onehot": 4
+            },
+            {
+                "_target_": "ToTensord",
+                "keys": [
+                    "pred",
+                    "label"
+                ],
+                "device": "@device"
+            },
+            {
+                "_target_": "SaveImaged",
+                "_disabled_": true,
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir",
+                "output_ext": ".json"
+            }
+        ]
+    },
+    "validate#handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@ckpt_dir + '/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        },
+        {
+            "_target_": "MetricsSaver",
+            "save_dir": "@output_dir",
+            "metrics": [
+                "val_f1",
+                "val_accuracy"
+            ],
+            "metric_details": [
+                "val_f1"
+            ],
+            "batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
+            "summary_ops": "*"
+        }
+    ],
+    "evaluating": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import scripts",
+        "$monai.data.register_writer('json', scripts.ClassificationWriter)",
+        "$@validate#evaluator.run()"
+    ]
+}

configs/inference.json

@@ -0,0 +1,116 @@
+{
+    "imports": [
+        "$import glob",
+        "$import json",
+        "$import pathlib",
+        "$import os"
+    ],
+    "bundle_root": "/workspace/data/pathology_nuclei_classification",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/data/CoNSePNuclei",
+    "images": "$list(sorted(glob.glob(@dataset_dir + '/Test/Images/*.png')))[:1]",
+    "labels": "$list(sorted(glob.glob(@dataset_dir + '/Test/Labels/*.png')))[:1]",
+    "input_data": "$[{'image': i, 'label': l} for i,l in zip(@images, @labels)]",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "DenseNet121",
+        "spatial_dims": 2,
+        "in_channels": 4,
+        "out_channels": 4
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "LoadImaged",
+                "keys": [
+                    "image",
+                    "label"
+                ],
+                "dtype": "uint8"
+            },
+            {
+                "_target_": "EnsureChannelFirstd",
+                "keys": [
+                    "image",
+                    "label"
+                ]
+            },
+            {
+                "_target_": "ScaleIntensityRanged",
+                "keys": "image",
+                "a_min": 0.0,
+                "a_max": 255.0,
+                "b_min": -1.0,
+                "b_max": 1.0
+            },
+            {
+                "_target_": "AddLabelAsGuidanced",
+                "keys": "image",
+                "source": "label"
+            }
+        ]
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "@input_data",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "DataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 4
+    },
+    "inferer": {
+        "_target_": "SimpleInferer"
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "Activationsd",
+                "keys": "pred",
+                "softmax": true
+            },
+            {
+                "_target_": "SaveImaged",
+                "keys": "pred",
+                "meta_keys": "pred_meta_dict",
+                "output_dir": "@output_dir",
+                "output_ext": ".json"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "CheckpointLoader",
+            "load_path": "$@bundle_root + '/models/model.pt'",
+            "load_dict": {
+                "model": "@network"
+            }
+        },
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "evaluating": [
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import scripts",
+        "$monai.data.register_writer('json', scripts.ClassificationWriter)",
+        "$@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 +1,86 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.0.5",
+    "changelog": {
+        "0.0.5": "add name tag",
+        "0.0.4": "Fix evaluation",
+        "0.0.3": "Update to use MONAI 1.1.0",
+        "0.0.2": "Update The Torch Vision Transform",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "1.1.0",
+    "pytorch_version": "1.13.0",
+    "numpy_version": "1.21.2",
+    "optional_packages_version": {
+        "nibabel": "4.0.1",
+        "pytorch-ignite": "0.4.9"
+    },
+    "name": "Pathology nuclei classification",
+    "task": "Pathology Nuclei classification",
+    "description": "A pre-trained model for Nuclei Classification within Haematoxylin & Eosin stained histology images",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_source": "consep_dataset.zip from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet",
+    "data_type": "png",
+    "image_classes": "RGB channel data, intensity scaled to [0, 1]",
+    "label_classes": "single channel data",
+    "pred_classes": "4 channels OneHot data, channel 0 is Other, channel 1 is Inflammatory, channel 2 is Epithelial, channel 3 is Spindle-Shaped",
+    "eval_metrics": {
+        "f1_score": 0.85
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [
+        "S. Graham, Q. D. Vu, S. E. A. Raza, A. Azam, Y-W. Tsang, J. T. Kwak and N. Rajpoot. \"HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images.\" Medical Image Analysis, Sept. 2019. https://doi.org/10.1016/j.media.2019.101563"
+    ],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "magnitude",
+                "format": "RGB",
+                "modality": "regular",
+                "num_channels": 4,
+                "spatial_shape": [
+                    128,
+                    128
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "R",
+                    "1": "G",
+                    "2": "B",
+                    "3": "Mask"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "probabilities",
+                "format": "classes",
+                "num_channels": 4,
+                "spatial_shape": [
+                    1,
+                    4
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1,
+                    2,
+                    3
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "Other",
+                    "1": "Inflammatory",
+                    "2": "Epithelial",
+                    "3": "Spindle-Shaped"
+                }
+            }
+        }
+    }
+}

configs/multi_gpu_evaluate.json

@@ -0,0 +1,32 @@
+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#handlers#1#_disabled_": "$dist.get_rank() > 0",
+    "evaluating": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import logging",
+        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$import scripts",
+        "$monai.data.register_writer('json', scripts.ClassificationWriter)",
+        "$@validate#evaluator.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/multi_gpu_train.json

@@ -0,0 +1,41 @@
+{
+    "device": "$torch.device(f'cuda:{dist.get_rank()}')",
+    "network": {
+        "_target_": "torch.nn.parallel.DistributedDataParallel",
+        "module": "$@network_def.to(@device)",
+        "device_ids": [
+            "@device"
+        ]
+    },
+    "train#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@train#dataset",
+        "even_divisible": true,
+        "shuffle": true
+    },
+    "train#dataloader#sampler": "@train#sampler",
+    "train#dataloader#shuffle": false,
+    "train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
+    "validate#sampler": {
+        "_target_": "DistributedSampler",
+        "dataset": "@validate#dataset",
+        "even_divisible": false,
+        "shuffle": false
+    },
+    "validate#dataloader#sampler": "@validate#sampler",
+    "validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
+    "training": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$import torch.distributed as dist",
+        "$dist.init_process_group(backend='nccl')",
+        "$torch.cuda.set_device(@device)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$import logging",
+        "$@train#trainer.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@validate#evaluator.logger.setLevel(logging.WARNING if dist.get_rank() > 0 else logging.INFO)",
+        "$@train#trainer.run()",
+        "$dist.destroy_process_group()"
+    ]
+}

configs/train.json

@@ -0,0 +1,350 @@
+{
+    "imports": [
+        "$import glob",
+        "$import ignite",
+        "$import json",
+        "$import pathlib",
+        "$import os"
+    ],
+    "bundle_root": "/workspace/data/pathology_nuclei_classification",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "dataset_dir": "/workspace/data/CoNSePNuclei",
+    "dataset_json": "$@dataset_dir + '/dataset.json'",
+    "train_datalist": "$json.loads(pathlib.Path(@dataset_json).read_text())['training']",
+    "val_datalist": "$json.loads(pathlib.Path(@dataset_json).read_text())['validation']",
+    "val_interval": 1,
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "network_def": {
+        "_target_": "DenseNet121",
+        "spatial_dims": 2,
+        "in_channels": 4,
+        "out_channels": 4
+    },
+    "network": "$@network_def.to(@device)",
+    "loss": {
+        "_target_": "torch.nn.CrossEntropyLoss"
+    },
+    "optimizer": {
+        "_target_": "torch.optim.Adam",
+        "params": "$@network.parameters()",
+        "lr": 0.0001
+    },
+    "max_epochs": 50,
+    "train": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "LoadImaged",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "dtype": "uint8"
+                },
+                {
+                    "_target_": "EnsureChannelFirstd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "SplitLabeld",
+                    "keys": "label",
+                    "mask_value": "",
+                    "others_value": 255,
+                    "to_binary_mask": false
+                },
+                {
+                    "_target_": "RandTorchVisiond",
+                    "keys": "image",
+                    "name": "ColorJitter",
+                    "brightness": 0.25,
+                    "contrast": 0.75,
+                    "saturation": 0.25,
+                    "hue": 0.04
+                },
+                {
+                    "_target_": "RandFlipd",
+                    "keys": [
+                        "image",
+                        "label",
+                        "others"
+                    ],
+                    "prob": 0.5
+                },
+                {
+                    "_target_": "RandRotate90d",
+                    "keys": [
+                        "image",
+                        "label",
+                        "others"
+                    ],
+                    "prob": 0.5
+                },
+                {
+                    "_target_": "ScaleIntensityRanged",
+                    "keys": "image",
+                    "a_min": 0.0,
+                    "a_max": 255.0,
+                    "b_min": -1.0,
+                    "b_max": 1.0
+                },
+                {
+                    "_target_": "AddLabelAsGuidanced",
+                    "keys": "image",
+                    "source": "label"
+                },
+                {
+                    "_target_": "SetLabelClassd",
+                    "keys": "label",
+                    "offset": -1
+                },
+                {
+                    "_target_": "SelectItemsd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                }
+            ]
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "@train_datalist",
+            "transform": "@train#preprocessing",
+            "cache_rate": 1.0,
+            "num_workers": 4
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@train#dataset",
+            "batch_size": 64,
+            "shuffle": true,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "Activationsd",
+                    "keys": "pred",
+                    "softmax": true
+                },
+                {
+                    "_target_": "AsDiscreted",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "argmax": [
+                        true,
+                        false
+                    ],
+                    "to_onehot": 4
+                },
+                {
+                    "_target_": "ToTensord",
+                    "keys": [
+                        "pred",
+                        "label"
+                    ],
+                    "device": "@device"
+                }
+            ]
+        },
+        "handlers": [
+            {
+                "_target_": "ValidationHandler",
+                "validator": "@validate#evaluator",
+                "epoch_level": true,
+                "interval": "@val_interval"
+            },
+            {
+                "_target_": "StatsHandler",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "tag_name": "train_loss",
+                "output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
+            },
+            {
+                "_target_": "scripts.TensorBoardImageHandler",
+                "class_names": {
+                    "0": "Other",
+                    "1": "Inflammatory",
+                    "2": "Epithelial",
+                    "3": "Spindle-Shaped"
+                },
+                "log_dir": "@output_dir",
+                "batch_limit": 4,
+                "tag_name": "train"
+            }
+        ],
+        "key_metric": {
+            "train_f1": {
+                "_target_": "ConfusionMatrix",
+                "metric_name": "f1 score",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "trainer": {
+            "_target_": "SupervisedTrainer",
+            "max_epochs": "@max_epochs",
+            "device": "@device",
+            "train_data_loader": "@train#dataloader",
+            "network": "@network",
+            "loss_function": "@loss",
+            "optimizer": "@optimizer",
+            "inferer": "@train#inferer",
+            "postprocessing": "@train#postprocessing",
+            "key_train_metric": "@train#key_metric",
+            "train_handlers": "@train#handlers",
+            "amp": true
+        }
+    },
+    "validate": {
+        "preprocessing": {
+            "_target_": "Compose",
+            "transforms": [
+                {
+                    "_target_": "LoadImaged",
+                    "keys": [
+                        "image",
+                        "label"
+                    ],
+                    "dtype": "uint8"
+                },
+                {
+                    "_target_": "EnsureChannelFirstd",
+                    "keys": [
+                        "image",
+                        "label"
+                    ]
+                },
+                {
+                    "_target_": "SplitLabeld",
+                    "keys": "label",
+                    "mask_value": "",
+                    "others_value": 255,
+                    "to_binary_mask": false
+                },
+                {
+                    "_target_": "ScaleIntensityRanged",
+                    "keys": "image",
+                    "a_min": 0.0,
+                    "a_max": 255.0,
+                    "b_min": -1.0,
+                    "b_max": 1.0
+                },
+                {
+                    "_target_": "AddLabelAsGuidanced",
+                    "keys": "image",
+                    "source": "label"
+                },
+                {
+                    "_target_": "SetLabelClassd",
+                    "keys": "label",
+                    "offset": -1
+                },
+                {
+                    "_target_": "SelectItemsd",
+                    "keys": [
+                        "image",
+                        "label",
+                        "image_meta_dict"
+                    ]
+                }
+            ]
+        },
+        "dataset": {
+            "_target_": "CacheDataset",
+            "data": "@val_datalist",
+            "transform": "@validate#preprocessing",
+            "cache_rate": 1.0
+        },
+        "dataloader": {
+            "_target_": "DataLoader",
+            "dataset": "@validate#dataset",
+            "batch_size": 64,
+            "shuffle": false,
+            "num_workers": 4
+        },
+        "inferer": {
+            "_target_": "SimpleInferer"
+        },
+        "postprocessing": "%train#postprocessing",
+        "handlers": [
+            {
+                "_target_": "StatsHandler",
+                "iteration_log": false
+            },
+            {
+                "_target_": "TensorBoardStatsHandler",
+                "log_dir": "@output_dir",
+                "iteration_log": false
+            },
+            {
+                "_target_": "CheckpointSaver",
+                "save_dir": "@ckpt_dir",
+                "save_dict": {
+                    "model": "@network"
+                },
+                "save_key_metric": true,
+                "key_metric_filename": "model.pt"
+            },
+            {
+                "_target_": "scripts.TensorBoardImageHandler",
+                "class_names": {
+                    "0": "Other",
+                    "1": "Inflammatory",
+                    "2": "Epithelial",
+                    "3": "Spindle-Shaped"
+                },
+                "log_dir": "@output_dir",
+                "batch_limit": 8,
+                "tag_name": "val"
+            }
+        ],
+        "key_metric": {
+            "val_f1": {
+                "_target_": "ConfusionMatrix",
+                "metric_name": "f1 score",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "additional_metrics": {
+            "val_accuracy": {
+                "_target_": "ignite.metrics.Accuracy",
+                "output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
+            }
+        },
+        "evaluator": {
+            "_target_": "SupervisedEvaluator",
+            "device": "@device",
+            "val_data_loader": "@validate#dataloader",
+            "network": "@network",
+            "inferer": "@validate#inferer",
+            "postprocessing": "@validate#postprocessing",
+            "key_val_metric": "@validate#key_metric",
+            "additional_metrics": "@validate#additional_metrics",
+            "val_handlers": "@validate#handlers",
+            "amp": true
+        }
+    },
+    "training": [
+        "$import sys",
+        "$sys.path.append(@bundle_root)",
+        "$monai.utils.set_determinism(seed=123)",
+        "$setattr(torch.backends.cudnn, 'benchmark', True)",
+        "$@train#trainer.run()"
+    ]
+}

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f254ae6b0318e1375d48c1e9d6056d236d4a1a32957afc4aeafba0e047c46b2b
+size 28419489

requirements.txt

@@ -0,0 +1,2 @@
+monai==1.1.0
+gradio