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Merge pull request #17 from MedVIC-Lab/publication-tags
Add publication tags
2 parents 43a8891 + 4844de8 commit e9b66f8

28 files changed

+133
-25
lines changed

‎layouts/publication.vue

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@@ -33,7 +33,6 @@ import { Content, useData } from 'vitepress';
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const { frontmatter } = useData();
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function getAuthorsList() {
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console.log(frontmatter.authors)
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return frontmatter.authors
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}
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</script>

‎pages/people.md

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@@ -14,6 +14,7 @@ const members = ref({
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researchers: [],
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phdStudents: [],
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msStudents: [],
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undergradStudents: [],
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staff: [],
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alumni: []
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})
@@ -77,6 +78,13 @@ onMounted(async () => {
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</template>
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</VPTeamPageSection>
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<VPTeamPageSection v-if="members.undergradStudents.length">
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<template #title>Undergrad Students</template>
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<template #members>
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<VPTeamMembers :members="members.msStudents" size="small" />
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</template>
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</VPTeamPageSection>
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<VPTeamPageSection v-if="members.staff.length">
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<template #title>Staff</template>
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<template #members>

‎pages/publications.md

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@@ -10,7 +10,7 @@ const SORT_OPTIONS = {
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ascending: false // boolean for ascending/descending order, true = ascending
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}
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const publications = ref([])
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const publications = ref([]);
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const sorting = ref({
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sort: "author",
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const search = ref("")
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const searchOpen = ref(false)
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const selectedTags = ref(new Set([]));
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const tagsOpen = ref(false);
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// helper
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function sortByAuthor(a, b, order) {
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const aFirstAuthor = a.authors.split(',')[0]
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const bFirstAuthor = b.authors.split(',')[0]
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const aLastName = aFirstAuthor.split(' ').pop()
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const bLastName = bFirstAuthor.split(' ').pop()
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return aLastName.localeCompare(bLastName) * order
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return aLastName.localeCompare(bLastName) * order;
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}
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const tags = computed(() => {
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const t = new Set([]);
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// get every unique tag from publications.value
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publications.value.forEach((p) => {
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if (p.tags) {
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// for every tag within the publication, add it to the Set object
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p.tags.forEach((tag) => t.add(tag));
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}
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});
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return t;
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});
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const sortedPublications = computed(() => {
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// filter search string first
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let filteredPubs = [...publications.value]
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if (search.value !== "") {
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filteredPubs = filteredPubs.filter((p) => p.title.toLowerCase().includes(search.value.toLowerCase()))
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}
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// filter by tags
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if (selectedTags.value.length > 0) {
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filteredPubs = filteredPubs.filter((p) => {
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if (!p.tags) return false
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return [...selectedTags.value].every(tag => p.tags.includes(tag))
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});
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}
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return filteredPubs.sort((a, b) => {
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const sortKey = sorting.value.sort
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const order = sorting.value.ascending ? -1 : 1
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}
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}
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function handleTagSelect(tag) {
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if (selectedTags.value.has(tag)) {
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selectedTags.value.delete(tag);
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} else {
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selectedTags.value.add(tag);
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}
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}
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function openSearchBar() {
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searchOpen.value = true
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searchOpen.value = true;
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}
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function openTagsMenu() {
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tagsOpen.value = true;
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}
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onMounted(async () => {
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height: 16px; /* Height of the icon */
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display: inline-block;
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}
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.tags {
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padding: 5px;
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max-width: 250px;
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}
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.tag {
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width: fit-content;
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}
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.v-chip--selected {
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background: var(--vp-c-brand-1);
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color: white;
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}
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</style>
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<span></span>
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<v-row
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</v-icon>
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<span v-else class="v-icon-placeholder"></span>
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</v-btn>
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<v-menu
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:close-on-content-click="false"
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location="bottom"
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>
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<template v-slot:activator="{ props }">
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<v-btn v-bind="props" icon="mdi-magnify" @click="() => {
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sorting.sort = previousSort // reset sorting to previous to ignore the click action
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openSearchBar()
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}">
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</v-btn>
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</template>
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<v-card min-width="300">
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<v-text-field
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v-model="search"
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hide-details
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label="Search"
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>
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</v-text-field>
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</v-card>
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</v-menu>
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<v-menu
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:close-on-content-click="false"
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location="bottom"
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>
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<template v-slot:activator="{ props }">
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<v-btn v-bind="props" icon="mdi-tag-outline" @click="() => {
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sorting.sort = previousSort // reset sorting to previous to ignore the click action
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openTagsMenu()
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}">
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</v-btn>
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</template>
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<v-card class="tags">
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<v-chip-group v-model="selectedTags" column multiple>
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<v-chip v-for="tag in tags" :key="tag" class="tag" :value="tag">
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{{tag}}
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</v-chip>
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</v-chip-group>
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</v-card>
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</v-menu>
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<v-menu
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:close-on-content-click="false"
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location="bottom"
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>
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<template v-slot:activator="{ props }">
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<v-btn v-bind="props" icon="mdi-magnify" @click="() => {
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sorting.sort = previousSort // reset sorting to previous to ignore the click action
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openSearchBar()
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}">
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</v-btn>
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</template>
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<v-card min-width="300">
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<v-text-field
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v-model="search"
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hide-details
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label="Search"
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>
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</v-text-field>
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</v-card>
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</v-menu>
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</v-btn-toggle>
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</v-row>
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‎pages/publications/2020_uncertain_deepssm.md

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@@ -4,6 +4,7 @@ title: "Uncertain-DeepSSM: From Images to Probabilistic Shape Models"
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authors: "Jadie Adams, Riddhish Bhalodia, Shireen Elhabian"
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conference: "Shape in Medical Imaging (ShapeMI) at MICCAI"
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year: "2020"
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tags: ["Deep Learning", "DeepSSM", "SSM"]
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links:
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archive: "https://arxiv.org/abs/2007.06516"
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image:

‎pages/publications/2021_benchmarking.md

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image:
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src: "2021_benchmarking.jpg"
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alt: Benchmarking FrameWork
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tags: ["SSM"]
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---
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# Benchmarking Off-the-shelf Statistical Shape Modeling Tools in Clinical Applications

‎pages/publications/2022_rvtr.md

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image:
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src: "2022_rvtr.jpg"
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alt: Results Highlight
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tags: ["SSM"]
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---
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# All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes

‎pages/publications/2022_sharedboundary.md

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image:
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src: "2022_stacom_shared.jpg"
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alt: Results Highlight
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tags: ["SSM", "Anatomy"]
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---
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# Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries

‎pages/publications/2022_spatiotemporal_ssm.md

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image:
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src: "2022_spatiotemporal_ssm.png"
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alt: Results Highlight
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tags: ["SSM", "Anatomy", "Cardiac"]
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---
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# Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach

‎pages/publications/2022_vib_deepssm.md

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image:
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src: "2022_vib_deepssm.png"
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alt: Results Highlight
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tags: ["DeepSSM", "SSM"]
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---
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# From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach

‎pages/publications/2023_ADASSM.md

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image:
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src: "2023_ADASSM.png"
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alt: Results Highlight
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tags: ["SSM"]
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---
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# ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images

‎pages/publications/2023_Automated_Annotation_pathology.md

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image:
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src: "2023_Automated_Annotation_pathology.png"
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alt: Automated Annotation
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tags: ['Deep Learning', 'Image Processing']
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---
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# End to End processing Pipeline for Automated Annotation using Immunohistochemistry.

‎pages/publications/2023_SCGAN_virtual_staining.md

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image:
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src: "2023_SCGAN_virtual_staining.png"
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alt: Structural CycleGAN
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tags: ['Deep Learning']
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# Propose a new architecture for Virtual Staining regularizing the learning via structural consistency.

‎pages/publications/2023_benchmarking_pathology_pretraining.md

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image:
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src: "2023_benchmarking_pathology_pretraining.png"
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alt: Benchmarking Semantic Segmentation
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tags: ['Image Processing', 'Segmentation']
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---
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# Benchmarking Whether Domain Specific pretraining helps in better gland and cell segmentation in histopathology

‎pages/publications/2023_benchmarking_segmentation.md

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image:
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src: "2023_benchmarking_segmentation.png"
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alt: Results Highlight
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tags: ['Segmentation']
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# Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation

‎pages/publications/2023_bvib_deepssm.md

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image:
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src: "2023_bvib_deepssm.png"
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alt: Results Highlight
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tags: ['DeepSSM', 'SSM']
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# Fully Bayesian VIB DeepSSM

‎pages/publications/2023_can_pointclouds.md

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src: "2023_can_pointclouds.png"
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alt: Results Highlight
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tags: ['SSM', 'Anatomy']
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# Can point cloud networks learn statistical shape models of anatomies?

‎pages/publications/2023_frontiers_sharedboundary.md

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alt: Results Highlight
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tags: ['SSM', 'Anatomy']
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# Statistical Shape Modeling of Multi-Organ Anatomies with Shared Boundaries: A Data-Driven Approach

‎pages/publications/2023_frontiers_spatiotemporal_ssm.md

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src: "2023_frontiers_spatiotemporal_ssm.jpg"
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alt: Results Highlight
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tags: ['SSM']
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# Learning Spatiotemporal Statistical Shape Models for Non-Linear Dynamic Anatomies

‎pages/publications/2023_mesh2ssm.md

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image:
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src: "2023_mesh2ssm.png"
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alt: Mesh2SSM Model
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tags: ['Deep Learning', 'SSM']
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# Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy

‎pages/publications/2024_MASSM.md

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src: "2024_MASSM.png"
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alt: Automated Annotation
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tags: ['Deep learning', 'SSM']
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# A method for multi-anatomy SSM directly from Images.

‎pages/publications/2024_SlicePropUQ.md

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year: "2024"
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links:
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publisher: "https://link.springer.com/chapter/10.1007/978-3-031-72117-5_26"
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tags: ['Segmentation', 'Image Processing']
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# Uncertainty Quantification for Slice Propagation Models

‎pages/publications/2024_UDA_Density_Matching.md

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src: "2024_UDA_Density_Matching.png"
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alt: UDA Density Matching
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tags: ['Deep Learning', 'Segmentation']
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# Method for Unsupervised Domain Adaptation using Target Density Matching

‎pages/publications/2024_point2ssm.md

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image:
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src: "2024_point2ssm.png"
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alt: Results Highlight
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tags: ['Deep Learning', 'SSM']
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# Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud

‎pages/publications/2024_scorp.md

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image:
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src: "2024_scorp.jpg"
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alt: Results Highlight
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tags: ['Image Processing', 'Deep Learning']
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# SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images

‎pages/publications/2024_spicorrnet.md

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src: "2024_spicorrnet.jpg"
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alt: Results Highlight
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tags: ['SSM', 'Deep Learning']
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# Probabilistic 3D Correspondence Prediction from Sparse Unsegmented Images

‎pages/publications/2024_weaklydeepssm.md

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src: "2024_weaklydeepssm.png"
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alt: Results Highlight
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tags: ['SSM', 'Deep Learning']
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# Weakly Supervised Bayesian Shape Modeling from Medical Images

‎pages/publications/2025_EfficientMorph.md

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image:
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src: "2025_EfficientMorph.png"
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alt: Efficient Architectures
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tags: ['Image Processing', 'Image Registration']
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---
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# A parameter efficient model for transformer based 3D registation

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