Upload MRIBrainSequenceBERT

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "MRIBrainSequenceBERT"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration.MRIBrainSequenceBERTConfig",
+    "AutoModel": "modeling.MRIBrainSequenceBERT"
+  },
+  "dropout": 0.2,
+  "max_len": 512,
+  "model_type": "mri_brain_sequence_bert",
+  "num_classes": 16,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0"
+}

configuration.py

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+from transformers import PretrainedConfig
+
+
+class MRIBrainSequenceBERTConfig(PretrainedConfig):
+    model_type = "mri_brain_sequence_bert"
+
+    def __init__(self, max_len=512, dropout=0.2, num_classes=16, **kwargs):
+        self.max_len = max_len
+        self.dropout = dropout
+        self.num_classes = num_classes
+        super().__init__(**kwargs)

modeling.py

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+import numpy as np
+import pydicom
+import torch
+import torch.nn as nn
+
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    PreTrainedModel,
+)
+
+from .configuration import MRIBrainSequenceBERTConfig
+
+
+class MRIBrainSequenceBERT(PreTrainedModel):
+    config_class = MRIBrainSequenceBERTConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.llm = AutoModelForSequenceClassification.from_pretrained(
+            "google/mobilebert-uncased"
+        )
+        self.dim_feats = self.llm.classifier.in_features
+        self.dropout = nn.Dropout(p=config.dropout)
+        self.classifier = nn.Linear(self.dim_feats, config.num_classes)
+        self.llm.dropout = nn.Identity()
+        self.llm.classifier = nn.Identity()
+
+        self.tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
+        self.max_len = config.max_len
+
+        self.metadata_elements = [
+            "SeriesDescription",
+            "ImageType",
+            "Manufacturer",
+            "ManufacturerModelName",
+            "ContrastBolusAgent",
+            "ScanningSequence",
+            "SequenceVariant",
+            "ScanOptions",
+            "MRAcquisitionType",
+            "SequenceName",
+            "AngioFlag",
+            "SliceThickness",
+            "RepetitionTime",
+            "EchoTime",
+            "InversionTime",
+            "NumberOfAverages",
+            "ImagingFrequency",
+            "ImagedNucleus",
+            "EchoNumbers",
+            "SpacingBetweenSlices",
+            "NumberOfPhaseEncodingSteps",
+            "EchoTrainLength",
+            "PercentSampling",
+            "PercentPhaseFieldOfView",
+            "PixelBandwidth",
+            "ContrastBolusVolume",
+            "ContrastBolusTotalDose",
+            "AcquisitionMatrix",
+            "InPlanePhaseEncodingDirection",
+            "FlipAngle",
+            "VariableFlipAngleFlag",
+            "SAR",
+            "dBdt",
+            "SeriesNumber",
+            "AcquisitionNumber",
+            "PhotometricInterpretation",
+            "PixelSpacing",
+            "ImagesInAcquisition",
+            "SmallestImagePixelValue",
+            "LargestImagePixelValue",
+        ]
+
+        self.label2index = {
+            "t1": 0,  # T1 precontrast
+            "t1c": 1,  # T1 postcontrast
+            "t2": 2,  # T2
+            "flair": 3,  # T2-FLAIR
+            "dwi": 4,  # DWI trace
+            "adc": 5,  # ADC map
+            "dti": 6,  # DTI
+            "swi": 7,  # SWI
+            "swi_mip": 8,  # SWI MinIP
+            "phase": 9,  # SWI phase images
+            "mag": 10,  # SWI mag images
+            "gre": 11,  # T2* GRE
+            "perf": 12,  # Perfusion-related images
+            "pd": 13,  # Proton density
+            "loc": 14,  # Localizers
+            "other": 15,  # Other, NOS
+        }
+
+        self.index2label = {v: k for k, v in self.label2index.items()}
+
+    def forward(
+        self, x: str, device: str | torch.device = "cpu", apply_softmax: bool = True
+    ):
+        x = self.tokenizer(
+            x,
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=self.max_len,
+        )
+
+        for k, v in x.items():
+            x[k] = v.to(device)
+
+        features = self.llm(**x)["logits"]
+        logits = self.classifier(self.dropout(features))
+        if apply_softmax:
+            logits = torch.softmax(logits, dim=1)
+        return logits
+
+    def create_string_from_dicom(
+        self, ds: pydicom.Dataset | dict, exclude_elements: list[str] = []
+    ):
+        # Sometimes we may want to exclude specific elements from being used for prediction
+        x = []
+        for each_element in self.metadata_elements:
+            # Only include elements which are present
+            if each_element in ds and each_element not in exclude_elements:
+                if ds[each_element] is not None and str(ds[each_element]) != "nan":
+                    x.append(f"{each_element} {ds[each_element]}")
+
+        x = " | ".join(x)
+        x = x.replace("[", "").replace("]", "").replace(",", "").replace("'", "")
+        return x
+
+    @staticmethod
+    def determine_plane_from_dicom(ds: pydicom.Dataset):
+        iop = np.asarray(ds.ImageOrientationPatient)
+        # Calculate the direction cosine for the normal vector of the plane
+        normal_vector = np.cross(iop[:3], iop[3:])
+
+        # Determine the plane based on the largest component of the normal vector
+        abs_normal = np.abs(normal_vector)
+        if abs_normal[0] > abs_normal[1] and abs_normal[0] > abs_normal[2]:
+            return "SAG"
+        elif abs_normal[1] > abs_normal[0] and abs_normal[1] > abs_normal[2]:
+            return "COR"
+        else:
+            return "AX"