ArticleComputational and mathematical methods in medicine2021
A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bringing AI to Clinicians: Simplifying Pleural Effusion Cytology Diagnosis with User-Friendly Models.Diagnostics (Basel, Switzerland) · 2025Article
- MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers.BMC medical informatics and decision making · 2025Article
- Will artificial intelligence (AI) replace cytopathologists: a scoping review of current applications and evidence of A.I. in urine cytology.World journal of urology · 2025Article
- New Challenges in Bladder Cancer Diagnosis: How Biosensing Tools Can Lead to Population Screening Opportunities.Sensors (Basel, Switzerland) · 2024Review
- A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.PloS one · 2024Article
- Article
- Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains underexplored. In this paper, we propose a novel deep learning model for cancer detection from urinary cytopathology screening images. We leverage recent ideas from the field of multioutput neural networks to provide a model that can efficiently train even on small-scale datasets, such as those typically found in real-world scenarios. Additionally, we argue that calibration (i.e., providing confidence levels that are aligned with the ground truth probability of an event) has been a major shortcoming of prior works, and we experiment a number of techniques to provide a well-calibrated model. We evaluate the proposed algorithm on a novel dataset, and we show that the combination of focal loss, multiple outputs, and temperature scaling provides a model that is significantly more accurate and calibrated than a baseline deep convolutional network.
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Registered trials
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