Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training

Adwaitt Pandya; Harita Bhargava; Ozioma C. Oguine; Shrikant Zade

arxiv: 2605.04008 · v1 · submitted 2026-05-05 · 💻 cs.CV · cs.LG

Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training

Adwaitt Pandya , Ozioma C. Oguine , Harita Bhargava , Shrikant Zade This is my paper

Pith reviewed 2026-05-07 17:20 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords brain tumor segmentation3D medical imagingSegResNetmulti-precision trainingDice scoredeep learningMRI segmentationtumor detection

0 comments

The pith

SegResNet trained with automatic multi-precision yields 0.84 Dice for 3D brain tumor segmentation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the SegResNet architecture, when trained with an automatic multi-precision method on 3D data, produces an overall Dice score of 0.84 along with region-specific scores of 0.90 for the whole tumor, 0.84 for the tumor core, and 0.79 for the enhancing tumor. It uses the Dice loss function and metric to drive and evaluate the segmentation. A sympathetic reader would care because brain tumors require early detection for better outcomes, and this training approach is positioned as enabling effective 3D identification of both benign and malignant cases in medical scans.

Core claim

We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.

What carries the argument

SegResNet architecture trained via automatic multi-precision method with Dice loss and Dice metric for 3D tumor segmentation.

If this is right

The reported scores enable region-specific segmentation of tumor core, whole tumor, and enhancing tumor areas.
Dice loss directly optimizes overlap accuracy for medical image segmentation tasks.
Automatic multi-precision training supports the model without manual precision specification.
The method addresses both benign steady-growth and malignant aggressive tumor patterns through 3D analysis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Lower memory use during training could result from the multi-precision approach, extending applicability to resource-limited clinical settings.
The same architecture and training could transfer to segmenting other 3D anatomical structures in MRI or CT scans.
Direct ablation against single-precision baselines would clarify how much the precision method contributes versus the base architecture.

Load-bearing premise

The automatic multi-precision training produces the reported Dice scores on a representative standard brain tumor dataset without undisclosed data selection, post-hoc tuning, or implementation artifacts.

What would settle it

Reproducing the SegResNet training with automatic multi-precision on a public 3D brain MRI dataset such as BraTS and obtaining overall Dice scores substantially below 0.80 would falsify the effectiveness claim.

read the original abstract

A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant expresses growth, making it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper applies SegResNet with mixed-precision training to brain tumor segmentation and reports Dice scores, but supplies none of the dataset, baseline, or ablation details needed to support its claims.

read the letter

The main takeaway is that this work runs the established SegResNet architecture with automatic multi-precision training and Dice loss on brain tumor data, then lists Dice scores of 0.84 overall, 0.84 on tumor core, 0.90 on whole tumor, and 0.79 on enhancing tumor. Nothing in the method is new. SegResNet and Dice loss have been standard in 3D medical segmentation for years, and mixed-precision training is a routine GPU optimization that most frameworks already support by default. The paper simply combines these pieces and records the resulting numbers. The subregion breakdown is a reasonable way to present results for this task, and the scores themselves fall in the range one would expect from similar models on common datasets. That is the extent of what the paper does well. The central problem is the complete lack of experimental grounding. The manuscript never names the dataset, never shows a single-precision baseline, never runs an ablation on the precision setting, and never reports error bars or validation protocol. Without those elements it is impossible to know whether the scores reflect the training trick, data selection, or implementation choices. The title and abstract frame the work as an enhancement, yet the evidence provided does not allow that framing to be checked. The citation pattern is correspondingly thin because the components are already well documented elsewhere. This paper might interest someone who wants a minimal working example of mixed-precision training on a segmentation network, but it adds nothing that a reader could build on or cite with confidence. I would not send it to peer review. The experimental gaps are large enough that referees could not fairly assess the main claim without major additions first.

Referee Report

3 major / 1 minor

Summary. The manuscript presents an approach to 3D brain tumor segmentation that implements the SegResNet architecture trained via an automatic multi-precision method. It employs the Dice loss function and reports Dice scores of 0.84 overall, 0.84 on the tumor core, 0.90 on the whole tumor, and 0.79 on the enhancing tumor.

Significance. If the reported Dice scores can be shown to result from the multi-precision training on a standard public dataset with appropriate controls, the work could provide a practical example of efficient training for volumetric medical segmentation models. At present the lack of experimental details prevents any assessment of whether the precision technique contributes meaningfully to the results.

major comments (3)

[Abstract] Abstract: The central performance claims (Dice scores of 0.84/0.84/0.90/0.79) cannot be interpreted without identification of the dataset used. No reference is made to any standard benchmark such as BraTS, nor is any description given of the number of cases, train/validation/test split, or preprocessing.
[Abstract] Abstract: No baseline comparison is supplied (e.g., the same SegResNet trained with single-precision or standard mixed-precision). Without this control it is impossible to attribute any performance difference to the automatic multi-precision method rather than implementation choices or data selection.
[Abstract] Abstract: The 'automatic multi-precision method' is mentioned only by name. The manuscript supplies no description of which precisions are used, how the automatic selection operates, which layers or operations are affected, or how the method differs from existing mixed-precision frameworks.

minor comments (1)

[Title] Title vs. abstract: The title uses 'Assorted Precision Training' while the text refers to 'automatic multi-precision method'; adopting a single consistent term would reduce ambiguity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important areas for clarification. We address each major comment point by point below and will revise the manuscript to provide the requested details, comparisons, and descriptions.

read point-by-point responses

Referee: [Abstract] Abstract: The central performance claims (Dice scores of 0.84/0.84/0.90/0.79) cannot be interpreted without identification of the dataset used. No reference is made to any standard benchmark such as BraTS, nor is any description given of the number of cases, train/validation/test split, or preprocessing.

Authors: We agree that the abstract omits essential dataset information needed to interpret the results. The work uses the BraTS benchmark. In the revised manuscript we will update the abstract to reference the BraTS dataset and add a methods subsection with the number of cases, train/validation/test splits, and preprocessing steps (including normalization and augmentation). revision: yes
Referee: [Abstract] Abstract: No baseline comparison is supplied (e.g., the same SegResNet trained with single-precision or standard mixed-precision). Without this control it is impossible to attribute any performance difference to the automatic multi-precision method rather than implementation choices or data selection.

Authors: We concur that baseline controls are required to isolate the contribution of the assorted-precision approach. The revised manuscript will include new experimental results for the identical SegResNet architecture trained under single-precision (FP32) and standard mixed-precision settings, with all other factors held constant, to enable direct attribution of performance differences. revision: yes
Referee: [Abstract] Abstract: The 'automatic multi-precision method' is mentioned only by name. The manuscript supplies no description of which precisions are used, how the automatic selection operates, which layers or operations are affected, or how the method differs from existing mixed-precision frameworks.

Authors: We acknowledge that the manuscript currently provides only a high-level reference to the method without implementation specifics. We will add a dedicated methods subsection describing the precisions employed, the automatic selection procedure, the layers and operations affected, and explicit differences from standard mixed-precision frameworks. revision: yes

Circularity Check

0 steps flagged

No derivation chain or self-referential structure present

full rationale

The manuscript is an empirical report of implementing SegResNet trained with automatic multi-precision on (unspecified) brain tumor data, using Dice loss and reporting Dice scores of 0.84 overall. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. All claims reduce to straightforward experimental outcomes rather than any closed loop of definition or construction, satisfying the default expectation of no significant circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract invokes no new free parameters, axioms, or invented entities. It rests on the pre-existing SegResNet architecture and the assumption that Dice loss is appropriate for this segmentation task, both drawn from prior literature.

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discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

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The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classiﬁcation

Baid U, Ghodasara S, Mohan S, Bilello M, Calabrese E, Colak E, et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classiﬁcation. arXiv [Preprint]. (2021)

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Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection

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work page doi:10.1016/j.nicl.2014.08 2014

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Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y , et al. ’Brain tumor segmentation with deep neural networks. arXiv [Preprint]. (2015)

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Pereira S, Pinto A, Alves V , Silva C. Brain tumor segmentation using convolutional neural networks in MRI images.IEEE Trans Med Imaging. (2016) 35:1240–51. doi: 10.1109/TMI.2016.2538465

work page doi:10.1109/tmi.2016.2538465 2016

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3D MRI brain tumor segmentation using autoencoder regularization

Myronenko A. 3D MRI brain tumor segmentation using autoencoder regularization. arXiv [Preprint]. (2018)

work page 2018

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, author Li, W

Wang G, Li W, Ourselin S, Vercauteren T. Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front Comput Neurosci. (2019) 13:56. doi: 10. 3389/fncom.2019.00056

work page arXiv 2019

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Supervised learning-based multimodal MRI brain tumor segmentation using texture features from supervoxels

Soltaninejad M, Y ang G, Lambrou T , Allinson N, Jones TL, Barrick TR, et al. Supervised learning-based multimodal MRI brain tumor segmentation using texture features from supervoxels. Comput. Methods Prog. Biomed. (2018) 157:69–84. doi: 10.1016/j.cmpb.2018.01.003

work page doi:10.1016/j.cmpb.2018.01.003 2018

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A two-stage cascade model with variational autoencoders and attention gates for MRI brain tumor segmentation

Lyu C, Shu H. A two-stage cascade model with variational autoencoders and attention gates for MRI brain tumor segmentation. Brainlesion. (2021):435–47. doi: 10.1007/978-3-030-72084-1_39

work page doi:10.1007/978-3-030-72084-1_39 2021

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Segmentation of brain tumors from MRI using deep learning

Iitm N. Segmentation of brain tumors from MRI using deep learning . (2019). Available online at: https://www.youtube.com/watch?v= PcNqA VNCZrE&t=331s

work page 2019

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Fuller, A., Millard, K., Green, J., 2023

Jadon S. A survey of loss functions for semantic segmentation. In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Proceedings of the 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) . Piscataway, NJ: IEEE (2020). doi: 10. 1109/cibcb48159.2020.9277638

work page arXiv 2020

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Metrics to evaluate your semantic segmentation model

Tiu E. Metrics to evaluate your semantic segmentation model. [online] Medium. (2021). Available online at: https://www.youtube.com/watch? v=PcNqA VNCZrE&t=331s(accessed November 13, 2021)

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Mixed precision training

Micikevicius P , Narang S, Alben J, Gregory FD, Elsen E, Garcia D, et al. Mixed precision training. arXiv [Preprint]. (2017)

work page 2017

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Spyridon B, Mauricio R, Andras J, Tefan Bauer S, Markus R, Alessandro C, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. Ithaca, NY: Cornell University (2019)

work page 2019

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Faster R-CNN: Towards real- time object detection with region proposal networks,

Badrinarayanan V , Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. (2017) 39:2481–95. doi: 10.1109/tpami.2016. 2644615

work page doi:10.1109/tpami.2016 2017