我正在尝试设计一个可以对细胞图像进行逐像素分割的 CNN。比如这些：
使用这样的分割掩码（除了每个原始图像的多个分割掩码，例如：单元格内部、单元格边界、背景）：
我主要从这里复制了 U-net 设计：https ://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
然而，即使是 10 个带注释的图像（超过 300 个单元格），我仍然得到相当糟糕的骰子系数分数，而且预测也不是很好。根据 U-Net 论文，这个数量的注释单元应该足以进行良好的预测。
这是我正在使用的模型的代码。
def get_unet():
inputs = Input((img_rows, img_cols, 1))
conv1 = Conv2D(16, window_size, activation='relu', padding='same')(inputs)
conv1 = Conv2D(16, window_size, activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, window_size, activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, window_size, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, window_size, activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, window_size, activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(128, window_size, activation='relu', padding='same')(pool3)
conv4 = Conv2D(128, window_size, activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, window_size, activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, window_size, activation='relu', padding='same')(conv5)
up6 = concatenate([Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
conv6 = Conv2D(128, window_size, activation='relu', padding='same')(up6)
conv6 = Conv2D(128, window_size, activation='relu', padding='same')(conv6)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
conv7 = Conv2D(128, window_size, activation='relu', padding='same')(up7)
conv7 = Conv2D(128, window_size, activation='relu', padding='same')(conv7)
up8 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
conv8 = Conv2D(64, window_size, activation='relu', padding='same')(up8)
conv8 = Conv2D(64, window_size, activation='relu', padding='same')(conv8)
up9 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
conv9 = Conv2D(16, window_size, activation='relu', padding='same')(up9)
conv9 = Conv2D(16, window_size, activation='relu', padding='same')(conv9)
conv10 = Conv2D(f_num, (1, 1), activation='softmax')(conv9) # change to N,(1,1) for more classes and softmax
model = Model(inputs=[inputs], outputs=[conv10])
model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])
return model`
我已经为模型尝试了许多不同的超参数，但都没有成功。骰子分数徘徊在 0.25 左右，我的损失在不同时期之间几乎没有减少。我觉得我在这里做一些根本错误的事情。有什么建议么？
编辑： Sigmoid 激活将骰子分数从 0.25 提高到 0.33（然而 1 个 epoch 再次达到这个分数，随后的 epoch 仅从 0.33 略微提高到 0.331 等）
dice_coef_loss 定义如下
smooth = 1.
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
另外，如果 model.summary 输出有用的话：
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) (None, 64, 64, 1) 0
_________________________________________________________________
conv2d_20 (Conv2D) (None, 64, 64, 16) 32
_________________________________________________________________
conv2d_21 (Conv2D) (None, 64, 64, 16) 272
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 32, 32, 16) 0
_________________________________________________________________
conv2d_22 (Conv2D) (None, 32, 32, 64) 1088
_________________________________________________________________
conv2d_23 (Conv2D) (None, 32, 32, 64) 4160
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_24 (Conv2D) (None, 16, 16, 128) 8320
_________________________________________________________________
conv2d_25 (Conv2D) (None, 16, 16, 128) 16512
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 8, 8, 128) 0
_________________________________________________________________
conv2d_26 (Conv2D) (None, 8, 8, 128) 16512
_________________________________________________________________
conv2d_27 (Conv2D) (None, 8, 8, 128) 16512
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_28 (Conv2D) (None, 4, 4, 512) 66048
_________________________________________________________________
conv2d_29 (Conv2D) (None, 4, 4, 512) 262656
_________________________________________________________________
conv2d_transpose_5 (Conv2DTr (None, 8, 8, 512) 1049088
_________________________________________________________________
concatenate_5 (Concatenate) (None, 8, 8, 640) 0
_________________________________________________________________
conv2d_30 (Conv2D) (None, 8, 8, 128) 82048
_________________________________________________________________
conv2d_31 (Conv2D) (None, 8, 8, 128) 16512
_________________________________________________________________
conv2d_transpose_6 (Conv2DTr (None, 16, 16, 128) 65664
_________________________________________________________________
concatenate_6 (Concatenate) (None, 16, 16, 256) 0
_________________________________________________________________
conv2d_32 (Conv2D) (None, 16, 16, 128) 32896
_________________________________________________________________
conv2d_33 (Conv2D) (None, 16, 16, 128) 16512
_________________________________________________________________
conv2d_transpose_7 (Conv2DTr (None, 32, 32, 128) 65664
_________________________________________________________________
concatenate_7 (Concatenate) (None, 32, 32, 192) 0
_________________________________________________________________
conv2d_34 (Conv2D) (None, 32, 32, 64) 12352
_________________________________________________________________
conv2d_35 (Conv2D) (None, 32, 32, 64) 4160
_________________________________________________________________
conv2d_transpose_8 (Conv2DTr (None, 64, 64, 64) 16448
_________________________________________________________________
concatenate_8 (Concatenate) (None, 64, 64, 80) 0
_________________________________________________________________
conv2d_36 (Conv2D) (None, 64, 64, 16) 1296
_________________________________________________________________
conv2d_37 (Conv2D) (None, 64, 64, 16) 272
_________________________________________________________________
conv2d_38 (Conv2D) (None, 64, 64, 4) 68
=================================================================
Total params: 1,755,092.0
Trainable params: 1,755,092.0
Non-trainable params: 0.0