Commit from GitHub Actions (Build Notebooks)

exercise.ipynb

@@ -396,6 +396,7 @@
    },
    "source": [
     "<div class=\"alert alert-info\">\n",
+    "\n",
     "## Training Results\n",
     "Please note down your thoughts about the following questions...\n",
     "<br><br>\n",
@@ -445,7 +446,7 @@
     "- [Pearson Correlation Coefficient (PCC)](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).\n",
     "\n",
     "Instance-level metrics via [Cellpose masks](https://cellpose.org/):\n",
-    "- [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification\n",
+    "- [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification)\n",
     "- [Jaccard Index](https://en.wikipedia.org/wiki/Jaccard_index)\n",
     "- [Dice Score](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient)\n",
     "- [Mean Average Precision](https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision)"
@@ -675,6 +676,7 @@
    },
    "outputs": [],
    "source": [
+    "\n",
     "# Define the function to perform  minmax normalization which is required for the pixel-level metrics.\n",
     "def min_max_scale(input):\n",
     "    return (input - np.min(input)) / (np.max(input) - np.min(input))\n",
@@ -719,14 +721,20 @@
     "    column=[\"psnr_nuc\"],\n",
     "    rot=30,\n",
     ")\n",
-    "test_pixel_metrics.head()\n",
-    "#%%[markdown]\n",
-    "\"\"\"\n",
+    "test_pixel_metrics.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46e19877",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
     "- What do these metrics tells us about the performance of the model?\n",
     "- How do the pixel-level metrics compare to the regression-based approach?\n",
-    "- Could these metrics be skewed by the presence of hallucinations or background pilxels in the virtual stains?\n",
-    "\n",
-    "\"\"\""
+    "- Could these metrics be skewed by the presence of hallucinations or background pilxels in the virtual stains?"
    ]
   },
   {

solution.ipynb

@@ -396,6 +396,7 @@
    },
    "source": [
     "<div class=\"alert alert-info\">\n",
+    "\n",
     "## Training Results\n",
     "Please note down your thoughts about the following questions...\n",
     "<br><br>\n",
@@ -445,7 +446,7 @@
     "- [Pearson Correlation Coefficient (PCC)](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient).\n",
     "\n",
     "Instance-level metrics via [Cellpose masks](https://cellpose.org/):\n",
-    "- [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification\n",
+    "- [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification)\n",
     "- [Jaccard Index](https://en.wikipedia.org/wiki/Jaccard_index)\n",
     "- [Dice Score](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient)\n",
     "- [Mean Average Precision](https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision)"
@@ -715,6 +716,7 @@
    },
    "outputs": [],
    "source": [
+    "\n",
     "# Define the function to perform  minmax normalization which is required for the pixel-level metrics.\n",
     "def min_max_scale(input):\n",
     "    return (input - np.min(input)) / (np.max(input) - np.min(input))\n",
@@ -759,14 +761,20 @@
     "    column=[\"psnr_nuc\"],\n",
     "    rot=30,\n",
     ")\n",
-    "test_pixel_metrics.head()\n",
-    "#%%[markdown]\n",
-    "\"\"\"\n",
+    "test_pixel_metrics.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83bbcee9",
+   "metadata": {
+    "cell_marker": "\"\"\"",
+    "lines_to_next_cell": 0
+   },
+   "source": [
     "- What do these metrics tells us about the performance of the model?\n",
     "- How do the pixel-level metrics compare to the regression-based approach?\n",
-    "- Could these metrics be skewed by the presence of hallucinations or background pilxels in the virtual stains?\n",
-    "\n",
-    "\"\"\""
+    "- Could these metrics be skewed by the presence of hallucinations or background pilxels in the virtual stains?"
    ]
   },
   {