# Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis

arXiv: https://arxiv.org/abs/2501.06223 · HTML: https://mlsanity.com/paper/2501.06223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT](https://mlsanity.com/paper/2307.01984/) (2023)
- [SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.10156/) (2024)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection](https://mlsanity.com/paper/2109.00374/) (2021)
- [Stochastic tissue window normalization of deep learning on computed tomography](https://mlsanity.com/paper/1912.00420/) (2019)
- [Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT](https://mlsanity.com/paper/2309.05405/) (2023)
- [Adaptive Local Window for Level Set Segmentation of CT and MRI Liver Lesions](https://mlsanity.com/paper/1606.03765/) (2016)
- [WindowNet: Learnable Windows for Chest X-ray Classification](https://mlsanity.com/paper/2306.06038/) (2023)

## Built on by (most cited first, 2 of 2)

- [ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges](https://mlsanity.com/paper/2512.17838/) (2025)
- [ITKIT: Feasible CT Image Analysis based on SimpleITK and MMEngine](https://mlsanity.com/paper/2603.14255/) (2026)

## Abstract (arXiv)

Whether during the early days of popularization or in the present, the window setting in Computed Tomography (CT) has always been an indispensable part of the CT analysis process. Although research has investigated the capabilities of CT multi-window fusion in enhancing neural networks, there remains a paucity of domain-invariant, intuitively interpretable methodologies for Auto Window Setting. In this work, we propose an plug-and-play module originate from Tanh activation function, which is compatible with mainstream deep learning architectures. Starting from the physical principles of CT, we adhere to the principle of interpretability to ensure the module's reliability for medical implementations. The domain-invariant design facilitates observation of the preference decisions rendered by the adaptive mechanism from a clinically intuitive perspective. This enables the proposed method to be understood not only by experts in neural networks but also garners higher trust from clinicians. We confirm the effectiveness of the proposed method in multiple open-source datasets, yielding 10%~200% Dice improvements on hard segment targets.