Multi-scale brain tumor segmentation combined with deep supervision

Yan, Bingbao; Cao, Miao; Gong, Weifang; Wei, Benzheng

doi:10.1007/s11548-021-02515-w

Cited by 5 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because precise manual segmentation of brain tumors is prohibitively time-consuming, the standard method of assessing for interval change is largely based on a radiologists' gestalt and bi-or triplanar measurements, which can overlook small changes in overall tumor size as well as subtle evolution in tumor heterogeneity and composition over time. Many studies have demonstrated that AI algorithms can match or exceed the segmentation performance of neuroradiologists in a fraction of the time [21][22][23][24][25][26].…”

Section: Segmentation and Preoperative Planningmentioning

confidence: 99%

Artificial intelligence in neuroimaging of brain tumors: reality or still promise?

Pan,

Huang

2023

Current Opinion in Neurology

View full text Add to dashboard Cite

Purpose of review To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption. Recent findings A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions. Nevertheless, most of these algorithms have yet to see widespread clinical adoption, given the lack of prospective studies demonstrating their efficacy and the logistical difficulties involved in clinical implementation. Summary While there has been significant progress in AI and neuro-oncologic imaging, clinical utility remains to be demonstrated. The next wave of progress in this area will be driven by prospective studies measuring outcomes relevant to clinical practice and go beyond retrospective studies which primarily aim to demonstrate high performance.

show abstract

Section: Segmentation and Preoperative Planningmentioning

confidence: 99%

Artificial intelligence in neuroimaging of brain tumors: reality or still promise?

Pan,

Huang

2023

Current Opinion in Neurology

View full text Add to dashboard Cite

show abstract

“…Additionally, the heterogeneity of the model’s several outputs can be used for UQ, similar to the ensemble methods mentioned above. This approach has already been used in clinical research utilizing deep learning models, such as breast cancer detection using breast ultrasound (7) and brain image semantic segmentation, e.g., brain tumors (8-10) and stroke lesions (11).…”

Section: Introductionmentioning

confidence: 99%

Deep Conformal Supervision: a comparative study

Vahdani,

Faghani

2024

Preprint

View full text Add to dashboard Cite

Background: Trustability is crucial for AI models in clinical settings. Conformal prediction as a robust uncertainty quantification framework has been receiving increasing attention as a valuable tool in improving model trustability. An area of active research is the method of non-conformity score calculation for conformal prediction. Method: We propose deep conformal supervision (DCS) which leverages the intermediate outputs of deep supervision for non-conformity score calculation, via weighted averaging based on the inverse of mean calibration error for each stage. We benchmarked our method on two publicly available datasets focused on medical image classification; a pneumonia chest radiography dataset and a preprocessed version of the 2019 RSNA Intracranial Hemorrhage dataset. Results: Our method achieved mean coverage errors of 16e-4 (CI: 1e-4, 41e-4) and 5e-4 (CI: 1e-4, 10e-4) compared to baseline mean coverage errors of 28e-4 (CI: 2e-4, 64e-4) and 21e-4 (CI: 8e-4, 3e-4) on the two datasets, respectively. Conclusion: In this non-inferiority study, we observed that the baseline results of conformal prediction already exhibit small coverage errors. Our method shows a relative enhancement, particularly noticeable in scenarios involving smaller datasets or when considering smaller acceptable error levels, although this improvement is not statistically significant. Keywords: Deep learning, Conformal prediction, Deep supervision, Uncertainty quantification, Classification

show abstract