Merge into main

.gitattributes

@@ -33,3 +33,5 @@ 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
+*safetensors filter=lfs diff=lfs merge=lfs -text
+*bin filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,117 @@
+import gradio as gr
+import os
+from npc_bert_models.gradio_demo import *
+from npc_bert_models.mlm_module import NpcBertMLM
+from npc_bert_models.cls_module import NpcBertCLS
+import json
+
+
+class main_window():
+    def __init__(self):
+        self.interface = None
+        self.examples = json.load(open("examples.json", 'r'))
+        
+    def initialize(self):
+        #! Initialize MLM
+        self.npc_mlm = NpcBertMLM()
+        self.npc_mlm.load()
+       
+        with gr.Blocks() as self.mlm_interface:
+            gr.Markdown("# Masked work prediction\n"
+                        "Enter any sentence. Use the token `[MASK]` to see what the model predicts.\n"
+                        "## Our examples:\n"
+                        "|Input masked sequence|Ground-truth masked word|\n"
+                        "|---------------------|------------------------|\n"
+                        + "\n".join([f"|{a}|{b}|" for a, b in zip(self.examples['mlm-inp'], self.examples['mlm-inp-GT'])]))
+            
+            with gr.Row():
+                with gr.Column():
+                    inp = gr.Textbox("The tumor is confined in the [MASK].", label='mlm-inp')
+                    btn = gr.Button("Run", variant='primary')
+                    
+                with gr.Column():
+                    out = gr.Label(num_top_classes=5)
+            
+
+            gr.Examples(self.examples['mlm-inp'], inputs=inp, label='mlm-inp')
+            btn.click(fn=self.npc_mlm.__call__, inputs=inp, outputs=out)
+            inp.submit(fn=self.npc_mlm.__call__, inputs=inp, outputs=out)
+
+        #! Initialize report classification
+        self.npc_cls = NpcBertCLS()
+        self.npc_cls.load()
+        
+        with gr.Blocks() as self.cls_interface:
+            gr.Markdown("""
+                        # Report discrimination
+                        
+                        In this example we explored how the fine-tuned BERT aids downstream task. We further trained it
+                        to do a simple task of discriminating between reports written for non-NPC patients and NPC patients.
+                        
+                        # Disclaimer
+                        
+                        The examples are mock reports that is created with reference to authentic reports, they do not
+                        represent any real patients. However, it was written to be an authentic representation of NPC or
+                        patient under investigation for suspected NPC but with negative imaging findings.
+                        """)
+            
+            with gr.Row():
+                with gr.Column():
+                    inp = gr.TextArea(placeholder="Use examples at the bottom to load example text reports.")
+                    inf = gr.File(file_types=['.txt'], label="Report file (plaintext)", show_label=True, interactive=True)
+                    inf.upload(fn=self._set_report_file_helper, inputs=inf, outputs=inp)
+                    inf.change(fn=self._set_report_file_helper, inputs=inf, outputs=inp)
+                    btn = gr.Button("Run", variant='primary')
+                    
+
+                with gr.Column():
+                    out = gr.Label(num_top_classes=2)
+                
+            # gr.Examples(examples=list(self.examples['reports'].values()), inputs=inp)
+            gr.Examples(examples="./report_examples", inputs=inf)
+            btn.click(fn=self.npc_cls.__call__, inputs=inp, outputs=out)
+            inp.submit(fn=self.npc_cls.__call__, inputs=inp, outputs=out)
+        
+        with gr.Blocks() as self.interface:
+            gr.Markdown("""
+                        # Introduction 
+                        
+                        This is a demo for displaying the potential of language models fine tunned using the carefully curated dataset
+                        of structured MRI radiology reports for nasopharyngeal carcinoma (NPC) examination. Our team has an established
+                        track record for researching the role of AI in early detectio for NPC. We have already developed an AI system
+                        with high sensitivty and specificity > 90%. However. we explanability of the system is currently a major challenge
+                        for translation. In fact, this is a general problem for AI's developement in radiology. Therefore, in this pilot 
+                        study, we investigate language model in understanding the context of the disease to explore the possibility of incorporating
+                        language model in our existing system for explanability. 
+                        
+                        # Affliations
+                        
+                        * Dr. M.Lun Wong, Dept. Imaging and Interventional Radiology. The Chinese University of Hong Kong
+                        
+                        # Disclaimer
+                        
+                        This software is provided as is and it is not a clinically validated software. The authors disclaim any responsibility
+                        arising as a consequence from using this demo. 
+                        """)
+            tabs = gr.TabbedInterface([self.mlm_interface, self.cls_interface], tab_names=["Masked Language Model", "Report classification"])
+
+    def lauch(self):
+        self.interface.launch()
+        pass
+            
+    def _set_report_file_helper(self, file_in):
+        try:
+            text = open(file_in, 'r').read()
+            return text
+        except:
+            print(f"Cannot read file {file_in}")
+            # Do nothing
+            pass
+        
+
+if __name__ == '__main__':
+    mw = main_window()
+    mw.initialize()
+    mw.lauch()
+    
+    

examples.json

@@ -0,0 +1,12 @@
+{
+  "mlm-inp": [
+    "A 2cm metastatic node is present in the right upper internal jugular chain [MASK] to the vein.", 
+    "The skull base and associated foraminal and [MASK] are unremarkable.", 
+    "The cavernous sinus and sections of the [MASK] included on the scan are unremarkable"
+  ],
+  "mlm-inp-GT": [
+    "posterior",
+    "fissures",
+    "cranium"
+  ]
+}

flagged/log.csv

@@ -0,0 +1,2 @@
+component 0,output,flag,username,timestamp
+There is a [MASK] in the nasopharynx.,"[{'score': 0.9277202486991882, 'token': 168, 'token_str': 'tumour', 'sequence': 'there is a tumour in the nasopharynx.'}, {'score': 0.008515788242220879, 'token': 245, 'token_str': 'carcinoma', 'sequence': 'there is a carcinoma in the nasopharynx.'}, {'score': 0.007673648186028004, 'token': 1531, 'token_str': 'asymmetry', 'sequence': 'there is a asymmetry in the nasopharynx.'}, {'score': 0.007609423715621233, 'token': 626, 'token_str': 'hyperplasia', 'sequence': 'there is a hyperplasia in the nasopharynx.'}, {'score': 0.0075225927866995335, 'token': 871, 'token_str': 'mass', 'sequence': 'there is a mass in the nasopharynx.'}]",,,2024-02-13 19:40:36.404239

models/npc-bert-best/config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

models/npc-bert-best/generation_config.json

@@ -0,0 +1,5 @@
+{
+  "_from_model_config": true,
+  "pad_token_id": 0,
+  "transformers_version": "4.37.2"
+}

models/npc-bert-best/model.safetensors

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models/npc-bert-best/special_tokens_map.json

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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

models/npc-bert-best/tokenizer_config.json

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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

models/npc-bert-best/training_args.bin

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models/npc-bert-best/vocab.txt

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+frozen
+disruption
+centimetre
+cricoarytenoid
+persistently
+encircle
+reconstruction
+remodelling
+response
+locoregional
+longitudinal
+extramedullary
+extramucosal
+undisrupted
+platysma
+jugulodiagastric
+relatives
+tinnitus
+10x20mm
+12aug2013
+12x13x16mm
+mechanical
+insufficiency
+aerodigestive
+descends
+subcentimeter
+presumabaly
+interspersed
+interhemispheric
+gangliocapsular
+nasoendoscopy
+t4n3disease
+anteroinferiorly
+20x8x16mm
+22x12x20mm
+22×37×26
+emphysema
+tracheoesophageal
+aforementioned
+blooming
+macroscopic
+epistaxis
+destroyed
+dyspraxia
+25x26x19mm
+retrophayrngeal
+retrophargyeal
+27x22x23mm
+27×17×31
+calvarial
+synchronous
+apxtsxcc
+settled
+impingement
+34×17×25
+nasophayrnx
+micropapillary
+microhaemorrhages
+23x11x23mm
+41x28x36mm
+foarmen
+supraclavivular
+13x10x15mm
+13x19x22mm
+cervicothoracic
+40×20×14
+42×25×30
+60×45×33
+7mar2012
+curvilinear
+documented
+established
+mteastases
+rudimentary
+waldeyers

models/npc-bert-cls/config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "npc_bert-best",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Benign/Healthy",
+    "1": "NPC"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Benign/Healthy": 0,
+    "NPC": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

models/npc-bert-cls/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0b76162187e0bc827c6178cb1351c15bb95a588d371028bab5c532de1d26e0f
+size 437958648

models/npc-bert-cls/special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

models/npc-bert-cls/tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

models/npc-bert-cls/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d21e77960bd0b33c5de1bf3b3173e81dd975fb8dc083a8f1359bedf017d94d9b
+size 4207

npc_bert_models/cls_module.py

@@ -0,0 +1,75 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline as hf_pipeline
+from pathlib import Path
+from typing import Any, Dict
+
+class NpcBertCLS():
+    r"""A class for performing report classification with BERT.
+
+    This class facilitates report classification tasks using a BERT model
+    fine-tuned on NPC staging reports. The base model is an uncased model
+    released by Microsoft, which can be found on the Hugging Face model hub
+    under the name 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext'.
+
+    Attributes:
+        model (transformers.PreTrainedModel): 
+            The fine-tuned BERT model for sequence classification.
+        tokenizer (transformers.PreTrainedTokenizer): 
+            The tokenizer for the BERT model.
+        pipeline (transformers.text-classification): 
+            The Hugging Face text-classification pipeline.
+        pretrained_model (str): 
+            The path to the directory containing the fine-tuned model.
+    """
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+        self.pipeline = None
+        # relative to app.py
+        self.pretrained_model = "./models/npc-bert-cls"
+    
+    def load(self) -> None:
+        """Loads the fine-tuned BERT model and related components.
+
+        This method initializes the model, tokenizer, and pipeline for the
+        text classification tasks using the pre-trained weights from the
+        specified directory.
+
+        Raises:
+            FileNotFoundError: If the pretrained model directory is not found.
+        """
+        if not Path(self.pretrained_model).is_dir():
+            raise FileNotFoundError(f"Cannot found pretrained model at: {self.pretrained_model}")
+        
+        self.model = AutoModelForSequenceClassification.from_pretrained(self.pretrained_model)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.pretrained_model)
+        self.pipeline = hf_pipeline("text-classification", model=self.model, tokenizer=self.tokenizer, device='cpu')
+    
+    def __call__(self, *args: Any) -> Any:
+        """Performs classification on the given reports.
+
+        This method should be called only after the `load` method has been executed
+        to ensure that the model and pipeline are properly initialized. It accepts
+        arguments to pass to the Hugging Face text-classification pipeline.
+
+        Args:
+            *args: Variable length argument list to pass to the pipeline.
+
+        Returns:
+            The output of the text-classification pipeline.
+
+        Raises:
+            BrokenPipeError: If the model has not been loaded before calling this method.
+        """
+        if self.pipeline is None:
+            msg = "Model was not initialized, have you run load()?"
+            raise BrokenPipeError(msg)
+        
+        # check length of text
+        if len(args[0]) < 10:
+            return "Not enough text for classification!" 
+        
+        pipe_out = self.pipeline(*args)
+        pipe_out = {o['label']: o['score'] for o in pipe_out}
+        return pipe_out
+        

npc_bert_models/gradio_demo.py

@@ -0,0 +1,8 @@
+# import gradio as gr
+# from .cls_module import NpcBertCLS
+# from .mlm_module import NpcBertMLM
+
+
+def greet(name):
+    return "Hello " + name
+

npc_bert_models/mlm_module.py

@@ -0,0 +1,74 @@
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+from transformers import pipeline as hf_pipeline
+from pathlib import Path
+
+class NpcBertMLM():
+    r"""A class for performing masked language modeling with BERT.
+
+    This class provides functionality to perform masked language modeling
+    predictions using a BERT model fine-tuned on NPC staging reports. The
+    base model used is an uncased model released by Microsoft, and it can be
+    found on the Hugging Face model hub under the name
+    'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext'.
+
+    Attributes:
+        model (transformers.PreTrainedModel): 
+            The fine-tuned BERT model.
+        tokenizer (transformers.PreTrainedTokenizer): 
+            The tokenizer for the BERT model.
+        pipeline (transformers.fill-mask): 
+            The Hugging Face fill-mask pipeline.
+        pretrained_model (str): The path to 
+            the directory containing the fine-tuned model.
+    """
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+        self.pipeline = None
+        # relative to app.py
+        self.pretrained_model = "./models/npc-bert-best"
+    
+    def load(self):
+        """Loads the fine-tuned BERT model and related components.
+
+        This method initializes the model, tokenizer, and pipeline for the
+        masked language modeling tasks using the pre-trained weights from the
+        specified directory.
+
+        Raises:
+            FileNotFoundError: If the pretrained model directory is not found.
+        """
+        if not Path(self.pretrained_model).is_dir():
+            raise FileNotFoundError(f"Cannot found pretrained model at: {self.pretrained_model}")
+        
+        self.model = AutoModelForMaskedLM.from_pretrained(self.pretrained_model)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.pretrained_model)
+        self.pipeline = hf_pipeline("fill-mask", model=self.model, tokenizer=self.tokenizer, device='cpu')
+    
+    def __call__(self, *args):
+        """Performs masked language modeling prediction.
+
+        This method should be called only after the `load` method has been executed
+        to ensure that the model and pipeline are properly initialized. It accepts
+        arguments to pass to the Hugging Face fill-mask pipeline.
+
+        Args:
+            *args: Variable length argument list to pass to the pipeline.
+
+        Returns:
+            The output of the fill-mask pipeline.
+
+        Raises:
+            BrokenPipeError: If the model has not been loaded before calling this method.
+        """
+        if self.pipeline is None:
+            msg = "Model was not initialized, have you run load()?"
+            raise BrokenPipeError(msg)
+        pipe_out = self.pipeline(*args)
+        # Just use the first output
+        if not isinstance(pipe_out[0], dict):
+            pipe_out = pipe_out[0]
+            
+        pipe_out = {oo['token_str']: oo['score'] for oo in pipe_out}
+        return pipe_out
+        

report_examples/NPC-report-1.txt

@@ -0,0 +1,24 @@
+History
+NPC for screening MRI
+
+MRI Nasopharynx and Neck.
+A full staging scan was performed in view of preliminary findings
+Axial T1W
+Coronal T2W
+Axial T2 SPIR
+Axial and coronal T1W post contrast
+Axial T1W FS post contrast
+
+REPORT:
+
+Primary Tumour
+There is a small primary nasopharyngeal carcinoma involving all walls, but predominantly the right side, of the nasopharynx.
+The nasal cavity, oropharynx and parapharyngeal regions are unremarkable. There are small bilateral otomastoid effusions.
+The skull base is unremarkable, except for a very small area of abnormal signal in the superior aspect of the clivus which is of uncertain significance. The paranasal sinuses are unremarkable. 
+The cavernous sinus and sections of the cranium included on the scan are unremarkable.
+
+Nodal Metastases
+There are a few metastatic right retropharyngeal nodes measuring up to 8mm in diameter as well as a couple of smaller left retropharyngeal nodes that are non specific. Shotty small nodes are seen in the remainder of the neck, which are not suspicious by MRI criteria.
+
+Conclusion
+Nasopharyngeal carcinoma predominantly in the right walls of the nasopharynx. There is possibly very early extension into the right cavernous sinus. Small volume metastatic right retropharyngeal nodes. T1/2 N1.

report_examples/NPC-report-2.txt

@@ -0,0 +1,28 @@
+MRI Nasopharynx and Neck.
+
+Axial T1W
+Coronal T2W
+Axial T2 SPIR
+Axial and coronal T1W post contrast
+
+REPORT:
+
+Primary Tumour
+There is a nasopharyngeal carcinoma involving predominantly the right side   of the nasopharynx.
+There is tumour extension into the right sided preclival muscles and early extension into the right parapharyngeal region. No tumour extension into the nasal cavity or oropharynx.
+Early skull base invasion into the right pterygoid process is present and tumour involves the right pterygopalatine fossa and abuts the right foramen lacerum. There are minor mucosal inflammatory changes in the paranasal sinuses.
+The cavernous sinus and sections of the cranium included on the scan are unremarkable.
+There is a right middle ear and mastoid effusion.
+
+Findings:
+The nasal cavity and oropharynx are not involved.
+The parapharyngeal regions are unremarkable and there are no middle ear or mastoid effusions.
+The skull base is unremarkable.
+The cavernous sinus and sections of the cranium included on the scan are unremarkable.
+
+Distant metastases 
+No distant metastases or second primary tumours are identified in the head and neck, upper thorax or bones.
+
+CONCLUSION
+1. Nasopharyngeal carcinoma with predominant mucosa and submucosa involvement.
+2.Enlarged nodes in retropharyngeal, jugulodigastric/upper jugular chain and right middle jugular regions. These are suspicious of nodal metastases.

report_examples/benign-report-1.txt

@@ -0,0 +1,11 @@
+MRI Nasopharynx
+Technique
+Axial T1W and T2SPIR
+Axial and coronal T1W post contrast
+
+Report
+A small focal area of mucosal thickening (depth approximately 8 mm) is present in the central portion of the superior roof of the nasopharynx which is only present on one slice and lies immediately below the skull base. In addition at this site there appears to be a defect just the right of the midline and it is uncertain whether this could be the site of a biopsy, please correlate clinically. The parapharyngeal regions are normal.
+Several small unremarkable upper cervical lymph nodes are noted.
+
+Conclusion
+Mild mucosal thickening is probably due to lymphoid hyperplasia.

report_examples/benign-report-2.txt

@@ -0,0 +1,18 @@
+MRI Nasopharynx
+
+Technique
+Screening protocol
+Axial T1W and T2SPIR
+Axial and coronal T1W post contrast
+
+Report
+There is very mild generalized thickening of the walls of the nasopharynx suggesting lymphoid hyperplasia.No nasopharyngeal carcinoma is identified.
+The parapharyngeal regions are normal.
+No cervical lymphadenopathy.
+In addition there is greater thickening in the central roof in the region of the adenoid, which is mildly asymmetrical, but no definite nasopharyngeal carcinoma is identified.
+Inflammatory mucosal thickening in the right maxillary sinus.
+There is a borderline enlarged left retropharyngeal node and very small right retropharyngeal and upper cervical nodes but no nodes with any definite metastatic features are identified.
+The remainder of the scan is unremarkable
+
+Conclusion
+Mild mucosal thickening is probably due to lymphoid hyperplasia.

requirements.txt

@@ -0,0 +1,4 @@
+pytorch >= 1.12.1
+transformers >= 4.37.2
+numpy >= 1.26
+gradio >= 4.18