Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation | Paper Citation Record

Observation 16d7a2ed-6bca-4619-a963-74d37ef9a47a · outbound

This paper cites Swin-unet: Unet-like pure transformer for medi- cal image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Swin-unet: Unet-like pure transformer for medi- cal image segmentation

Reference 1

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Observation fc120fa7-cb44-49fc-9e94-3f67c84c6df4 · outbound

This paper cites Few-shot semantic segmentation with prototype learning.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few-shot semantic segmentation with prototype learning

Reference 6

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Observation 2d123463-68da-4ac9-b173-76c0f9c9ca00 · outbound

This paper cites Concur- rent multimodality image segmentation by active contours for radiotherapy treatment planning a.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Concur- rent multimodality image segmentation by active contours for radiotherapy treatment planning a

Reference 7

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This paper cites Ca-net: Com- prehensive attention convolutional neural networks for ex- plainable medical image segmentation.IEEE Transactions on Medical Imaging, 40(2):699–711,.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Ca-net: Com- prehensive attention convolutional neural networks for ex- plainable medical image segmentation.IEEE Transactions on Medical Imaging, 40(2):699–711,

Reference 11

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Observation e3f28e15-34bf-4657-ac9c-d01fdd9101e1 · outbound

This paper cites Anomaly detection- inspired few-shot medical image segmentation through self-supervision with supervoxels.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Anomaly detection- inspired few-shot medical image segmentation through self-supervision with supervoxels

Reference 12

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Observation 56896202-2bb8-4d3b-ae62-94af74b6d78a · outbound

This paper cites Algorithm as 136: A k-means clustering algorithm.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Algorithm as 136: A k-means clustering algorithm

Reference 13

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Observation 983b5840-4bfc-4876-ba21-7d6c33b85cbd · outbound

This paper cites Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation

Reference 16

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Observation ecbe2bbe-41cc-43f4-abd3-c68b02f0a2e8 · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.215470Z

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Observation b8a61540-87b4-4650-b03b-905205fd32f1 · outbound

This paper cites Microsoft coco: Com- mon objects in context.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Microsoft coco: Com- mon objects in context

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.764921Z

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Observation 04eb66c5-c9e6-4577-ac22-a73794f4ae05 · outbound

This paper cites Semi-supervised medical image seg- mentation through dual-task consistency.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Semi-supervised medical image seg- mentation through dual-task consistency

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.312198Z

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Observation 3d645b0e-fdf6-4c73-96e2-4cf57639cf6f · outbound

This paper cites Self- supervision with superpixels: Training few-shot medical image segmentation without annotation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Self- supervision with superpixels: Training few-shot medical image segmentation without annotation

Reference 23

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Observation d6319391-f336-4649-a1c5-b594fc6943a6 · outbound

This paper cites Self- supervised learning for few-shot medical image segmenta- tion.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Self- supervised learning for few-shot medical image segmenta- tion

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.067090Z

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Observation 6990ead4-7d43-431c-af48-2ed1d83623b4 · outbound

This paper cites A robust volumetric transformer for accurate 3d tumor segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation A robust volumetric transformer for accurate 3d tumor segmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.950067Z

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Observation 828eb9c7-a746-424e-916b-edc49c60f5c5 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.815396Z

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Observation bdf877b6-ad53-4a2d-a751-405d9abcc283 · outbound

This paper cites ‘squeeze & excite’guided few-shot segmentation of volu- metric images.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation ‘squeeze & excite’guided few-shot segmentation of volu- metric images

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.647177Z

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Observation c4a0b00b-486d-43fc-96d3-21b5430bf3e8 · outbound

This paper cites Imagenet large scale visual recogni- tion challenge.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Imagenet large scale visual recogni- tion challenge

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.523581Z

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Observation 11e7fcee-f7b5-4e79-95c0-cc792c5d1b1b · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation One-Shot Learning for Semantic Segmentation

Reference 29

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Observation 4ed044e7-2a58-4f9f-8d5d-e0efcd3636df · outbound

This paper cites Q-net: Query-informed few-shot medical image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Q-net: Query-informed few-shot medical image segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.362994Z

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Observation d120ec68-68e6-4b04-9b8e-b7bddbcdaa22 · outbound

This paper cites Marginal loss and exclusion loss for par- tially supervised multi-organ segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Marginal loss and exclusion loss for par- tially supervised multi-organ segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.238752Z

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Observation 25e944ce-cc13-44d9-bb45-9621188caaa6 · outbound

This paper cites Prototypical networks for few-shot learning.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Prototypical networks for few-shot learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.068969Z

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Observation c484e27b-7110-4ac9-851f-d6d3b1ee547e · outbound

This paper cites Learning to compare: Relation network for few-shot learn- ing.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Learning to compare: Relation network for few-shot learn- ing

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.941972Z

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Observation 8fb82f9f-b2d2-49ed-ae8f-c0f637fc48bf · outbound

This paper cites Few-shot medical image segmentation with high-fidelity prototypes.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few-shot medical image segmentation with high-fidelity prototypes

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.770331Z

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Observation 663ad6ed-8720-436b-a312-3ee6105c1e77 · outbound

This paper cites Integrat- ing segmentation information into cnn for breast cancer diagnosis of mammographic masses.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Integrat- ing segmentation information into cnn for breast cancer diagnosis of mammographic masses

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.581563Z

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Observation 9e52b68b-2b15-4476-868b-7cd1980a93f5 · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9(11),.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Visualizing data using t-sne.Journal of machine learning research, 9(11),

Reference 36

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Observation 5ea63fda-aa93-4329-b55c-fef73cf6c443 · outbound

This paper cites Panet: Few-shot im- age semantic segmentation with prototype alignment.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Panet: Few-shot im- age semantic segmentation with prototype alignment

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.963080Z

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Observation 5482ef2e-b52f-4fa8-b9a8-5a1f3af2106d · outbound

This paper cites Few-shot medical image segmentation regularized with self-reference and contrastive learning.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few-shot medical image segmentation regularized with self-reference and contrastive learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.804663Z

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Observation 11a43a9f-fa7f-4e3c-afcc-c8160edf7890 · outbound

This paper cites Vessel-net: Retinal vessel segmentation under multi- path supervision.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Vessel-net: Retinal vessel segmentation under multi- path supervision

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.650192Z

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Observation 1d7a852a-dbb1-4d67-8225-abc69b53b580 · outbound

This paper cites Dual contrastive learning with anatomical auxiliary supervision for few-shot medical image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Dual contrastive learning with anatomical auxiliary supervision for few-shot medical image segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.442547Z

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Observation f38dfed5-fac6-4e57-8ca6-82c8bcd991c4 · outbound

This paper cites Prototype mixture models for few-shot semantic segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.301925Z

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Observation df75a418-ca13-476b-89ea-c2d15b367550 · outbound

This paper cites Pet-guided delineation of radiation therapy treatment vol- umes: a survey of image segmentation techniques.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Pet-guided delineation of radiation therapy treatment vol- umes: a survey of image segmentation techniques

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.169363Z

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Observation 15b169b8-d5c0-46ba-aa38-7d8cfc3ad8f0 · outbound

This paper cites Few-shot medical image segmenta- tion via a region-enhanced prototypical transformer.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few-shot medical image segmenta- tion via a region-enhanced prototypical transformer

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:05.819519Z

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Observation e745c685-8bba-4ab7-99c2-1db0dda07a73 · outbound

This paper cites Multivariate mixture model for myocardial segmentation combining multi-source im- ages.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Multivariate mixture model for myocardial segmentation combining multi-source im- ages

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:05.629749Z

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Observation 029f1e40-9e77-4f51-9ffe-0c99c894489d · outbound

This paper cites Algorithm of HPG.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Algorithm of HPG

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:05.483301Z

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Observation dfd23e85-b34d-4795-9ad8-d4f9619ae30b · outbound

This paper cites Deep learning techniques for medical image segmentation: achievements and challenges.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Deep learning techniques for medical image segmentation: achievements and challenges

Reference 1979

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Observation 4defca2a-fbcd-4636-87ea-228a95ebe05a · outbound

This paper cites Multi- organ segmentation over partially labeled datasets with multi-scale feature abstraction.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Multi- organ segmentation over partially labeled datasets with multi-scale feature abstraction

Reference 2007

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Observation 2ac47efd-7607-4143-973f-a70964084a7f · outbound

This paper cites Matching networks for one shot learning.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Matching networks for one shot learning

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.370384Z

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Observation dac077fa-a075-486e-becd-121552113c07 · outbound

This paper cites Few-shot 3d volumetric segmentation with multi-surrogate fusion.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few-shot 3d volumetric segmentation with multi-surrogate fusion

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:05.969503Z

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Observation 8ed877ec-d219-4d53-a2a8-6945a427d011 · outbound

This paper cites Few shot medical image segmentation with cross attention transformer.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few shot medical image segmentation with cross attention transformer

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.569520Z

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Observation 601056c5-b1e0-4307-a68a-6cfe16f88692 · outbound

This paper cites H- denseunet: hybrid densely connected unet for liver and tu- mor segmentation from ct volumes.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation H- denseunet: hybrid densely connected unet for liver and tu- mor segmentation from ct volumes

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.987094Z

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Observation 3d899662-4f2d-42e0-bcc2-7256d64a84da · outbound

This paper cites Deepigeos: a deep interactive geodesic framework for medical image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Deepigeos: a deep interactive geodesic framework for medical image segmentation

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.172675Z

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Observation 816f3e72-6072-476c-b1ab-d87b9756288f · outbound

This paper cites Few-Shot Learning with Graph Neural Networks.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few-Shot Learning with Graph Neural Networks

Reference 2017

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Observation c120f03c-f76e-48bd-bcfd-e574c7b9b50b · outbound

This paper cites A deep learning-based auto-segmentation system for organs-at- risk on whole-body computed tomography images for ra- diation therapy.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation A deep learning-based auto-segmentation system for organs-at- risk on whole-body computed tomography images for ra- diation therapy

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.868380Z

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Observation 0b6fa465-1a91-49a5-ae04-9967594704e1 · outbound

This paper cites nnu-net: a self-configuring method for deep learning- based biomedical image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation nnu-net: a self-configuring method for deep learning- based biomedical image segmentation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.543567Z

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Observation 93723343-ddbf-4585-ac48-5324904231fe · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Model-agnostic meta-learning for fast adaptation of deep networks

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:11.759563Z

Source-reported events for the cited work

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Observation e8e37534-4f2c-4dcc-a1fa-eb3bdb698c19 · outbound

This paper cites Few- shot medical image segmentation via generating multiple representative descriptors.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Few- shot medical image segmentation via generating multiple representative descriptors

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.702746Z

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Observation 9a1fda77-f0cf-482c-8e64-3b15a639fe4f · outbound

This paper cites Drinet for medical image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Drinet for medical image segmentation

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.970002Z

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Observation 0fcc4ec0-b9ab-445e-a15c-d2abc3d85c36 · outbound

This paper cites Fully convolutional networks for seman- tic segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Fully convolutional networks for seman- tic segmentation

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.413521Z

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Observation 8e345bc9-cc65-48db-83f2-de580190b346 · outbound

This paper cites Hyperdense-net: a hyper-densely connected cnn for multi-modal image segmentation.

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation Hyperdense-net: a hyper-densely connected cnn for multi-modal image segmentation

Reference 2024

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