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bodypartxr

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Jason Adrian commited on Oct 17, 2023

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95b697c

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1 Parent(s): c80976f

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app.py +52 -2
figures/resnet-residual-block-for-resnet18-from-scratch-using-pytorch.png +0 -0
figures/resnet18-basic-blocks-1.png +0 -0
index.html +51 -0
sample/1.2.392.200036.9125.4.0.1964921730.2349552188.1786966286.dcm.jpeg +0 -0
sample/10.127.133.1137.156.1251.20190404101039.dcm.jpeg +0 -0
sample/1b6a707131f787fe37d3ea40d2011d43.dicom.jpeg +0 -0
sample/2e3204c2bb7a8fcdd6ec1ed547e2967e.dicom.jpeg +0 -0
sample/badaec3e4d5f382ebf0b51ba2c917cea.dicom.jpeg +0 -0
style.css +83 -0
utils/page_utils.py +51 -0

app.py CHANGED Viewed

@@ -4,6 +4,7 @@ from torchvision.transforms import transforms
 import numpy as np
 from typing import Optional
 import torch.nn as nn
 class BasicBlock(nn.Module):
     """ResNet Basic Block.
@@ -148,6 +149,8 @@ model.eval()
 class_names = ['abdominal', 'adult', 'others', 'pediatric', 'spine']
 class_names.sort()
 transformation_pipeline = transforms.Compose([
     transforms.ToPILImage(),
     transforms.Grayscale(num_output_channels=1),
@@ -206,5 +209,52 @@ def image_classifier(inp):
     return labeled_result
-demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
-demo.launch(share=True)

 import numpy as np
 from typing import Optional
 import torch.nn as nn
+import os
 class BasicBlock(nn.Module):
     """ResNet Basic Block.
 class_names = ['abdominal', 'adult', 'others', 'pediatric', 'spine']
 class_names.sort()
+examples_dir = "sample"
 transformation_pipeline = transforms.Compose([
     transforms.ToPILImage(),
     transforms.Grayscale(num_output_channels=1),
     return labeled_result
+# gradio code block for input and output
+with gr.Blocks() as app:
+    gr.Markdown("# Lung Cancer Classification")
+with open('index.html', encoding="utf-8") as f:
+    description = f.read()
+# gradio code block for input and output
+with gr.Blocks(theme=gr.themes.Default(primary_hue=page_utils.KALBE_THEME_COLOR, secondary_hue=page_utils.KALBE_THEME_COLOR).set(
+        button_primary_background_fill="*primary_600",
+        button_primary_background_fill_hover="*primary_500",
+        button_primary_text_color="white",
+    )) as app:
+    with gr.Column():
+        gr.HTML(description)
+    with gr.Row():
+        with gr.Column():
+            inp_img = gr.Image()
+            with gr.Row():
+                clear_btn = gr.Button(value="Clear")
+                process_btn = gr.Button(value="Process", variant="primary")
+        with gr.Column():
+            out_txt = gr.Label(label="Probabilities", num_top_classes=3)
+    process_btn.click(image_classifier, inputs=inp_img, outputs=out_txt)
+    clear_btn.click(lambda:(
+        gr.update(value=None),
+        gr.update(value=None)
+        ),
+        inputs=None,
+        outputs=[inp_img, out_txt])
+    gr.Markdown("## Image Examples")
+    gr.Examples(
+        examples=[os.path.join(examples_dir, "1.2.392.200036.9125.4.0.1964921730.2349552188.1786966286.dcm.jpeg"),
+                  os.path.join(examples_dir, "1b6a707131f787fe37d3ea40d2011d43.dicom.jpeg"),
+                  os.path.join(examples_dir, "2e3204c2bb7a8fcdd6ec1ed547e2967e.dicom.jpeg"),
+                  os.path.join(examples_dir, "10.127.133.1137.156.1251.20190404101039.dcm.jpeg"),
+                  os.path.join(examples_dir, "badaec3e4d5f382ebf0b51ba2c917cea.dicom.jpeg"),
+                 ],
+        inputs=inp_img,
+        outputs=out_txt,
+        fn=image_classifier,
+        cache_examples=False,
+    )
+# demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+app.launch(share=True)

figures/resnet-residual-block-for-resnet18-from-scratch-using-pytorch.png ADDED Viewed

figures/resnet18-basic-blocks-1.png ADDED Viewed

index.html ADDED Viewed

	@@ -0,0 +1,51 @@

+<!DOCTYPE html>
+<html>
+	<head>
+		<link rel="stylesheet" href="file/style.css" />
+		<link rel="preconnect" href="https://fonts.googleapis.com" />
+		<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+		<link href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600;700&display=swap" rel="stylesheet" />
+		<title><strong>Body Part Classification</strong></title>
+	</head>
+	<body>
+		<div class="container">
+			<h1 class="title"><strong> Body Part Classification</strong></h1>
+			<h2 class="subtitle"><strong>Kalbe Digital Lab</strong></h2>
+			<section class="overview">
+				<div class="grid-container">
+					<h3 class="overview-heading"><span class="vl">Overview</span></h3>
+					<p class="overview-content">
+						The Body Part Classification program serves the critical purpose of categorizing body parts from DICOM x-ray scans into five distinct classes: abdominal, adult chest, pediatric chest, spine, and others. This program trained using ResNet18 model.
+					</p>
+				</div>
+				<div class="grid-container">
+					<h3 class="overview-heading"><span class="vl">Dataset</span></h3>
+					<div>
+						<p class="overview-content">
+						The program has been meticulously trained on a robust and diverse dataset, specifically <a href="https://vindr.ai/datasets/bodypartxr" target="_blank">VinDrBodyPartXR Dataset.</a>.
+						<br/>
+						This dataset is introduced by Vingroup of Big Data Institute which include 16,093 x-ray images that are collected and manually annotated. It is a highly valuable resource that has been instrumental in the training of our model.
+						</p>
+						<ul>
+							<li>Objective: Body Part Identification</li>
+							<li>Task: Classification</li>
+							<li>Modality: Grayscale Images</li>
+						</ul>
+					</div>
+				</div>
+				<div class="grid-container">
+					<h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
+					<div>
+						<p class="overview-content">
+							The model architecture of ResNet18 to train x-ray images for classifying body part.
+						</p>
+						<img class="content-image" src="file/figures/resnet18-basic-blocks-1.png" alt="model-architecture" />
+						<img class="content-image" src="file/figures/resnet-residual-block-for-resnet18-from-scratch-using-pytorch.png" alt="model-architecture" />
+					</div>
+				</div>
+			</section>
+			<h3 class="overview-heading"><span class="vl">Demo</span></h3>
+			<p class="overview-content">Please select or upload a body part x-ray scan image to see the capabilities of body part classification with this model</p>
+		</div>
+	</body>
+</html>

sample/1.2.392.200036.9125.4.0.1964921730.2349552188.1786966286.dcm.jpeg ADDED Viewed

sample/10.127.133.1137.156.1251.20190404101039.dcm.jpeg ADDED Viewed

sample/1b6a707131f787fe37d3ea40d2011d43.dicom.jpeg ADDED Viewed

sample/2e3204c2bb7a8fcdd6ec1ed547e2967e.dicom.jpeg ADDED Viewed

sample/badaec3e4d5f382ebf0b51ba2c917cea.dicom.jpeg ADDED Viewed

style.css ADDED Viewed

	@@ -0,0 +1,83 @@

+* {
+	box-sizing: border-box;
+}
+body {
+	font-family: 'Source Sans Pro', sans-serif;
+	font-size: 16px;
+}
+.container {
+	width: 100%;
+	margin: 0 auto;
+}
+.title {
+	font-size: 24px !important;
+	font-weight: 600 !important;
+	letter-spacing: 0em;
+	text-align: center;
+	color: #374159 !important;
+}
+.subtitle {
+	font-size: 24px !important;
+	font-style: italic;
+	font-weight: 400 !important;
+	letter-spacing: 0em;
+	text-align: center;
+	color: #1d652a !important;
+	padding-bottom: 0.5em;
+}
+.overview-heading {
+	font-size: 24px !important;
+	font-weight: 600 !important;
+	letter-spacing: 0em;
+	text-align: left;
+}
+.overview-content {
+	font-size: 14px !important;
+	font-weight: 400 !important;
+	line-height: 30px !important;
+	letter-spacing: 0em;
+	text-align: left;
+}
+.content-image {
+	width: 100% !important;
+	height: auto !important;
+}
+.vl {
+	border-left: 5px solid #1d652a;
+	padding-left: 20px;
+	color: #1d652a !important;
+}
+.grid-container {
+	display: grid;
+	grid-template-columns: 1fr 2fr;
+	gap: 20px;
+	align-items: flex-start;
+	margin-bottom: 0.7em;
+}
+.grid-container:nth-child(2) {
+	align-items: center;
+}
+@media screen and (max-width: 768px) {
+	.container {
+		width: 90%;
+	}
+	.grid-container {
+		display: block;
+	}
+	.overview-heading {
+		font-size: 18px !important;
+	}
+}

utils/page_utils.py ADDED Viewed

	@@ -0,0 +1,51 @@

+from typing import Optional
+class ColorPalette:
+    """Color Palette Container."""
+    all = []
+    def __init__(
+        self,
+        c50: str,
+        c100: str,
+        c200: str,
+        c300: str,
+        c400: str,
+        c500: str,
+        c600: str,
+        c700: str,
+        c800: str,
+        c900: str,
+        c950: str,
+        name: Optional[str] = None,
+    ):
+        self.c50 = c50
+        self.c100 = c100
+        self.c200 = c200
+        self.c300 = c300
+        self.c400 = c400
+        self.c500 = c500
+        self.c600 = c600
+        self.c700 = c700
+        self.c800 = c800
+        self.c900 = c900
+        self.c950 = c950
+        self.name = name
+        ColorPalette.all.append(self)
+KALBE_THEME_COLOR = ColorPalette(
+    name='kalbe',
+    c50='#f2f9e8',
+    c100='#dff3c4',
+    c200='#c2e78d',
+    c300='#9fd862',
+    c400='#7fc93f',
+    c500='#3F831C',
+    c600='#31661a',
+    c700='#244c13',
+    c800='#18340c',
+    c900='#0c1b06',
+    c950='#050a02',
+)