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Asset Detail:
Identifying cancerous lung nodules without the presence of annotated slides for reference
Asset Detail:
Identifying cancerous lung nodules without the presence of annotated slides for reference
Overview
| ASSET LINK: | https://modac.cancer.gov/assetDetails?dme_data_id=NCI-DME-MS01-98736825 |
| PROGRAM NAME: | NCI Data Challenges |
| STUDY NAME: | NCI Cancer Research Data Commons (CRDC) Artificial Intelligence Data-Readiness (AIDR) Challenge |
| ASSET NAME: | Identifying cancerous lung nodules without the presence of annotated slides for reference |
| ASSET PATH: | /NCI_DOE_Archive/challenges/crdc_aidr_challenge/agnes_mcfarlin_tier1_submission |
Asset Attributes
| ATTRIBUTE | VALUE |
|---|---|
| ASSET NAME | Identifying cancerous lung nodules without the presence of annotated slides for reference |
| ASSET DESCRIPTION | This asset contains the submission from Agnes McFarlin, the second place winner of the 2024 AI Data Readiness Challenge for the NCI Cancer Research Data Commons (CRDC) Tier 1: Training an AI/ML model with single modal data. In this tier, participants must train an AI/ML model utilizing data from a single data class. Their submission focused on Category 4 (Diagnosis). General use case: Classify cancer cells versus healthy cells in a specific tissue. Specific use case: Use of radiological images from Imaging Data Commons, Cancer Imaging Archive, and Cancer Data Access System to identify cancerous lung nodules. |
| ASSET IDENTIFIER | agnes_mcfarlin_tier1_submission |
| ASSET TYPE | Model |
| MODEL DOMAIN | classification |
| MODEL FRAMEWORK | PyTorch |
| MODEL PLATFORM | None |
| PLATFORM VERSION | None |
| POC NAME | Agnes McFarlin |
| POC EMAIL | amcfarlin1991@gmail.com |
| IS MODEL DEPLOYED | No |
| COLLECTION SIZE | 3.8 MB |
| CURATION STATUS | Unverified |
Asset Files
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| FILE/COLLECTION | FILE SIZE | ACTIONS |
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