ArticleJournal of imaging2021
Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Multi-parametric MRI synthesis for glioblastoma from quantitative MR fingerprinting: Quantitative synthetic neural network (QS-Net).Medical physics · 2026Article
- An end-to-end pipeline for automated fetal brain segmentation and biometry from 3D SSFP MRI.Frontiers in neuroscience · 2026Article
- Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping.Dermatopathology (Basel, Switzerland) · 2025Article
- An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations.Virchows Archiv : an international journal of pathology · 2025Article
- Deep Learning Image Processing Models in Dermatopathology.Diagnostics (Basel, Switzerland) · 2025Review
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep-Learning-Based AI-Model for Predicting Dental Plaque in the Young Permanent Teeth of Children Aged 8-13 Years.Children (Basel, Switzerland) · 2025Article
- AI-Based Detection of Optical Microscopic Images ofInformation (Basel) · 2025Article
- [Pancreas segmentation with multi-channel convolution and combined deep supervision].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- geodl: An R package for geospatial deep learning semantic segmentation using torch and terra.PloS one · 2024Article
- Article
- Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks.Cancers · 2023Article
- Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI.Cancers · 2022Article
- Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images.Journal of fungi (Basel, Switzerland) · 2022Article
- StainCUT: Stain Normalization with Contrastive Learning.Journal of imaging · 2022Article
- Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.Cancers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues' exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network's decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.