# Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data arXiv: https://arxiv.org/abs/2310.15098 ยท HTML: https://mlsanity.com/paper/2310.15098/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 59 references that are in the map. - First posted on arXiv 23 Oct 2023. ## Builds on (most cited first, 30 of 31) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020) - [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018) - [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021) - [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023) - [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016) - [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021) - [Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation](https://mlsanity.com/paper/1502.02734/) (2015) - [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020) - [BoxInst: High-Performance Instance Segmentation with Box Annotations](https://mlsanity.com/paper/2012.02310/) (2020) - [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015) - [On Regularized Losses for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1803.09569/) (2018) - [Normalized Cut Loss for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1804.01346/) (2018) - [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019) - [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021) - [Weakly-Supervised Salient Object Detection via Scribble Annotations](https://mlsanity.com/paper/2003.07685/) (2020) - [Pointly-Supervised Instance Segmentation](https://mlsanity.com/paper/2104.06404/) (2021) - [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022) - [Progressively Normalized Self-Attention Network for Video Polyp Segmentation](https://mlsanity.com/paper/2105.08468/) (2021) - [Deep Gradient Learning for Efficient Camouflaged Object Detection](https://mlsanity.com/paper/2205.12853/) (2022) - [CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision](https://mlsanity.com/paper/2203.01475/) (2022) - [Multi-source weak supervision for saliency detection](https://mlsanity.com/paper/1904.00566/) (2019) - [Going to Extremes: Weakly Supervised Medical Image Segmentation](https://mlsanity.com/paper/2009.11988/) (2020) - [Geometry Constrained Weakly Supervised Object Localization](https://mlsanity.com/paper/2007.09727/) (2020) - [CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation](https://mlsanity.com/paper/1806.04051/) (2018) - [Cascaded V-Net using ROI masks for brain tumor segmentation](https://mlsanity.com/paper/1812.11588/) (2018) - [Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma](https://mlsanity.com/paper/1807.02941/) (2018) - [Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution](https://mlsanity.com/paper/2212.01579/) (2022) - [Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis](https://mlsanity.com/paper/2308.03008/) (2023) - [CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans](https://mlsanity.com/paper/2301.12291/) (2023) ## Built on by (most cited first, 9 of 9) - [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024) - [AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking](https://mlsanity.com/paper/2407.16697/) (2024) - [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024) - [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024) - [Learning Segmentation from Radiology Reports](https://mlsanity.com/paper/2507.05582/) (2025) - [RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology](https://mlsanity.com/paper/2605.10761/) (2026) - [Report Supervision](https://mlsanity.com/paper/2608.27668/) (2026) - [RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports](https://mlsanity.com/paper/2609.35637/) (2026) - [PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning](https://mlsanity.com/paper/2609.37350/) (2026) ## Abstract (arXiv) Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.