Multi-task learning of a deep k-nearest neighbour network for histopathological image classification and retrieval

可解释性 计算机科学 人工智能 深度学习 机器学习 模式识别(心理学) 散列函数 上下文图像分类 人工神经网络 k-最近邻算法 任务(项目管理) 图像(数学) 数据挖掘 计算机安全 经济 管理
作者
Tingying Peng,Melanie Boxberg,Wilko Weichert,Nassir Navab,Carsten Marr
标识
DOI:10.1101/661454
摘要

Abstract Deep neural networks have achieved tremendous success in image recognition, classification and object detection. However, deep learning is often criticised for its lack of transparency and general inability to rationalize its predictions. The issue of poor model interpretability becomes critical in medical applications, as a model that is not understood and trusted by physicians is unlikely to be used in daily clinical practice. In this work, we develop a novel multi-task deep learning framework for simultaneous histopathology image classification and retrieval, leveraging on the classic concept of k-nearest neighbors to improve model interpretability. For a test image, we retrieve the most similar images from our training databases. These retrieved nearest neighbours can be used to classify the test image with a confidence score, and provide a human-interpretable explanation of our classification. Our original framework can be built on top of any existing classification network (and therefore benefit from pretrained models), by (i) adding a triplet loss function with a novel triplet sampling strategy to compare distances between samples and (ii) a Cauchy hashing loss function to accelerate neighbour searching. We evaluate our method on colorectal cancer histology slides, and show that the confidence estimates are strongly correlated with model performance. The explanations provided by nearest neighbors are intuitive and useful for expert evaluation by giving insights into understanding possible model failures, and can support clinical decision making by comparing archived images and patient records with the actual case.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
2秒前
潇湘雪月发布了新的文献求助10
3秒前
3秒前
3秒前
4秒前
感动黄豆发布了新的文献求助10
4秒前
hhhblabla应助东方红采纳,获得10
6秒前
Poker应助sb采纳,获得10
7秒前
Ginger发布了新的文献求助10
7秒前
吃骨头的猫完成签到,获得积分10
7秒前
小李完成签到,获得积分10
7秒前
7秒前
8秒前
明芬发布了新的文献求助30
10秒前
10秒前
Smile完成签到,获得积分10
10秒前
Chaoe完成签到,获得积分10
13秒前
建国发布了新的文献求助10
14秒前
闪闪w发布了新的文献求助10
17秒前
淡烟流水完成签到,获得积分10
17秒前
俏皮芷蕊完成签到,获得积分10
18秒前
完美世界应助忐忑的阑香采纳,获得10
18秒前
华仔应助兴奋千兰采纳,获得10
23秒前
Ginger完成签到,获得积分10
24秒前
潇湘雪月发布了新的文献求助10
27秒前
科研通AI5应助科研通管家采纳,获得10
29秒前
29秒前
佳琳有乐完成签到,获得积分10
29秒前
29秒前
小蘑菇应助科研通管家采纳,获得10
29秒前
大模型应助科研通管家采纳,获得10
29秒前
CHAosLoopy应助科研通管家采纳,获得10
30秒前
桐桐应助科研通管家采纳,获得10
30秒前
今后应助cccyq采纳,获得10
30秒前
烟花应助科研通管家采纳,获得30
30秒前
情怀应助科研通管家采纳,获得10
30秒前
30秒前
隐形曼青应助科研通管家采纳,获得10
30秒前
在水一方应助科研通管家采纳,获得30
30秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989115
求助须知:如何正确求助?哪些是违规求助? 3531367
关于积分的说明 11253688
捐赠科研通 3269986
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882078
科研通“疑难数据库(出版商)”最低求助积分说明 809105